2). To answer this question, we experiment with integrating different flow networks into our mobile video object detection system. previous approach towards object tracking and detection using video sequences through different phases. It reports accuracy on a subset of ImageNet VID, where the split is not publicly known. The technical report of Fast YOLO [51] is also very related. ∙ Given a non-key frame i, the feature propagation from key frame k to frame i is denoted as. object detection in video. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C. ∙ Shufflenet: An extremely efficient convolutional neural network for Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. We further studied several design choices in flow-guided GRU. It is also unclear whether the Because the recognition on the key frame is still not fast enough. However, [20] aggregates feature maps from nearby frame in a linear and memoryless way. share, There has been significant progresses for image object detection in rece... 802–810. share, In this paper, we propose an efficient and fast object detector which ca... At the same time, object recognition has also come to the fore. 16 Apr 2018 • Xizhou Zhu • Jifeng Dai • Xingchi Zhu • Yichen Wei • Lu Yuan. A flow-guided GRU module is proposed for effective feature aggregation. Our system surpasses all the existing systems by clear margin. The objects can generally be identified from either pictures or video feeds. Specifically, given two succeeding key frames k and k′, the aggregated feature at frame k′ is computed by. 0 The detection network Ndet is applied on ^Fk′ to get detection predictions for the key frame k′. Karpathy, A., Khosla, A., Bernstein, M., Berg, A., Li, F.F. The proposed system 0 representations. In previous two-stage detectors, either the detection head or its previous layer, is of heavy-weight. Li, Z., Gavves, E., Jain, M., Snoek, C.G. It is randomly initialized and jointly trained with Nfeat. In encoder part, convolution is always the bottleneck of computation. Figure 3 shows the speed-accuracy curves of our method with and without flow guidance. modeling. 12/04/2018 ∙ by Liangzhe Yuan, et al. Xizhou Zhu [0] Jifeng Dai (代季峰) [0] Lu Yuan (袁路) [0] Yichen Wei (危夷晨) [0] computer vision and pattern recognition, 2018. Third, we apply GRU only on sparse key frames (e.g., every 10th) instead of consecutive frames. 11/30/2017 ∙ by Xizhou Zhu, et al. Without all these important components, its accuracy cannot compete with ours. It consists of classifying an image into one of many different categories. Download PDF. In this paper, we propose a light weight network for video object detection on mobile devices. ∙ Object detection is a computer vision technique whose aim is to detect objects such as cars, buildings, and human beings, just to mention a few. Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. To improve detection accuracy, flow-guided feature aggregation (FGFA) [20] aggregates feature maps from nearby frames, which are aligned well through the estimated flow. Object detection in static images has achieved significant progress in recent years using deep CNN [1]. recognition. Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: Light-head r-cnn: In defense of two-stage object detector. 11/27/2018 ∙ by Shiyao Wang, et al. It is worth noting that it achieves higher accuracy than FlowNet Half and FlowNet Inception utilized in [19], with at least one order less computation overhead. Augmented reality has been on the rise due to the proliferation of mobile devices. With increased key frame duration length, the accuracy drops gracefully as the computation overhead relieves. Other lightweight image object detectors should be generally applicable within our system. During inference, feature maps on any non-key frame i are propagated from its preceding key frame k by. Smagt, P., Cremers, D., Brox, T.: Flownet: Learning optical flow with convolutional networks. Instead, multi-resolution predictions are up-sampled to the same spatial resolution with the finest prediction, and then are averaged as the final prediction. Such design choices are vital towards high performance video object detection. Additionally, we also exploit a light image object detector for computing features on key frame, which leverage advanced and efficient techniques, such as depthwise separable convolution [22] and Light-Head R-CNN [23]. Compared with the original GRU [40], there are three key differences. It is one order faster than the best previous effort on fast object detection, with on par accuracy (see Figure 1). For training simply use … In particular, many studies have focused on object recognition based on markerless matching. The proposed techniques are unified to an end-to-end learning system. : Videolstm convolves, attends and flows for action recognition. FlowNet [32] is originally proposed for pixel-level optical flow estimation. share, Despite the recent success of video object detection on Desktop GPUs, its Second, ϕ is ReLU function instead of hyperbolic tangent function (tanh) for faster and better convergence. Following the practice in [48, 49], model training and evaluation are performed on the 3,862 training video snippets and the 555 validation video snippets, respectively. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., In this paper, we propose an efficient and fast object detector which ca... Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, Final result yi for frame Ii incurs a loss against the ground truth annotation. It shows better speed-accuracy performance than the single-stage detectors. 9. Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision First, 3×3 convolution is used instead of fully connected matrix multiplication, since fully connected matrix multiplication is too costly when GRU is applied to image feature maps. Feature aggregation should be operated on aligned feature maps according to flow. (2015) A more cheaper Nflow is so necessary. Curves are drawn with varying image resolutions. Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Main difficulty here was to deal with video stream going into and coming from the container. It cannot speedup upon the single-frame baseline without sparse key frames. The two dimensional motion field Mi→k between two frames Ii and Ik is estimated through a flow network Nflow(Ik,Ii)=Mi→k, which is much cheaper than Nfeat. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., precipitation nowcasting. Also, identify the gap and suggest a new approach to improve the tracking of object over video frame. Xizhou Zhu, Jifeng Dai, Xingchi Zhu, Yichen Wei, Lu Yuan Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. of input size through a class of convolutional layers. Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: Convolutional lstm network: A machine learning approach for There has been significant progresses for image object detection in recent years. performed. Lightweight image object detector is an indispensable component for our video object detection system. A possible issue with the current approach is that there would be short latency in processing online streaming videos. TensorFlow: Large-scale machine learning on heterogeneous systems Meanwhile, YOLOv2, SSDLite and Tiny YOLO obtain accuracies of 58.7%, 57.1%, and 44.1% at frame rates of 0.3, 3.8 and 2.2 fps respectively. Learning Region Features for Object Detection Jiayuan Gu*, Han Hu, Liwei Wang, Yichen Wei, and Jifeng Dai European Conference on Computer Vision (ECCV), 2018. architecture is still far too heavy for mobiles. Towards High Performance Video Object Detection for Mobiles. Though recursive aggregation [21]. Recently, [39] has showed that Gated Recurrent Unit (GRU) [40] is more powerful in modeling long-term dependencies than LSTM [41] and RNN [42], because nonlinearities are incorporated into the network state updates. (shorter side for image object detection network in {320, 288, 256, 224, 208, 192, 176, 160}), for fair comparison. An mAP score of 58.4% is achieved by the aggregation approach in [21], which is comparable with the single frame baseline at 6.5× theoretical speedup. By default, α and β are set as 1.0. In the forward pass, Ik−(n−1)l is assumed as a key frame, and the inference pipeline is exactly performed. Transferring image-based object detectors to domain of videos remains a For non-key frames, sparse feature propagation is We do not dive into the details of varying technical designs. Discrimination, Object detection at 200 Frames Per Second. In its improvements, like SSDLite [50] and Tiny SSD [17], more efficient feature extraction networks are also utilized. To speedup flow network Nflow greatly, we present Light Flow, a more light weight flow network with several deliberate designs based on FlowNet [32]. Flow estimation is the key to feature propagation and aggregation. For our system, the curve is drawn also by adjusting the image size111the input image resolution of the flow network is kept to be half of the resolution of the image recognition network. No code available yet. Our method achieves an accuracy of 60.2% at 25.6 fps. We first carefully reproduced their results in paper (on PASCAL VOC [52] and COCO [53]), and then trained models on ImageNet VID, also by utilizing ImageNet VID and ImageNet DET train sets. Bibliographic details on Towards High Performance Video Object Detection for Mobiles. ∙ The accuracy of our method at long duration length (l=20) is still on par with that of the single frame baseline, and is 10.6× more computationally efficient. Dollár, P., Zitnick, C.L. This paper describes a light weight network architecture for mobile video object detection. In SGD, n+1 nearby video frames, Ii, Ik, Ik−l, Ik−2l, …, Ik−(n−1)l, 0≤i−k
=n�_o�G3�k~CkwG��W �+��/�쫑�x��Vi&�^t}��_�ݠ��/��y�b v��}o��=��ͨ��Pv����ɋ7�� ' Abstract: There has been significant progresses for image object detection in recent years. share, We propose a light-weight video frame interpolation algorithm. It is worth noting that the accuracy further drops if no flow is applied even for sparse feature propagation on the non-key frames. detection from videos. 1, . Would such a light-weight flow network effectively guide feature propagation? My own dataset contains 2150 images for training and 540 for test. YOLO frames object detection as a regression problem, and a light-weight detection head directly predicts bounding boxes on the whole image. For Light-Head R-CNN, a 1×1 convolution with 10×7×7 filters was applied followed by a 7×7 groups position-sensitive RoI warping [6]. ... We propose a light-weight video frame interpolation algorithm. ∙ Built upon the recent works, this work proposes a unified viewpoint based on the principle of multi-frame end-to-end learning of features and cross-frame motion. The whole network can be trained end-to-end. Directly applying these detectors to video object detection faces challenges from two aspects. 0 Inference time is evaluated with TensorFlow Lite [18] on a single 2.3GHz Cortex-A72 processor of Huawei Mate 8. The MobileNet module is pre-trained on ImageNet classification task [47]. Relation Networks for Object Detection In encoder, the input is converted into a bundle of feature maps in spatial dimensions to 1/64. First, applying the deep networks on all video frames introduces unaffordable computational cost. For SSD, the output space of bounding boxes are discretized into a set of anchor boxes, which are classified by a light-weight detection head. In: Advances in neural information processing systems. For example, flow estimation, as the key and common component in feature propagation and aggregation. Both two systems cannot compete with the proposed system. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. Faster r-cnn: Towards real-time object detection with region proposal We remove the ending average pooling and the fully-connected layer of MobileNet [13], and retain the convolutional layers. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., ... Request PDF | Towards High Performance Video Object Detection for Mobiles | Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. share, Deep convolutional neutral networks have achieved great success on image... networks. mb model size. 12/16/2017 ∙ by Congrui Hetang, et al. I am applying tensorflow object detection api to build a model to detect a single object. The trained network is either applied on trimmed sequences of the same length as in training, or on the untrimmed video sequences without specific length restriction. In this paper, we present a light weight network architecture for video object detection on mobiles. On sparse key frame, we present flow-guided Gated Recurrent Unit (GRU) based feature aggregation, an effective aggregation on a memory-limited platform. 30 object categories are involved, which are a subset of ImageNet DET annotated categories. To the best of our knowledge, for the first time, we achieve realtime video object detection on mobile with reasonably good accuracy. Moreover, the incidents are detected very fast. The key frame duration length is every 10 frames. How important is to exploit flow to align features across frames? Towards High Performance Video Object Detection. But it neither reports accuracy nor has public code. A flow-guided GRU module is designed to effectively aggregate Mobile video object detection with temporally-aware feature maps. Table 3 presents the results of training and inference on frame sequences of varying lengths. Abstract: Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. Log In Sign Up. networks. Otherwise, displacements caused by large object motion would cause severe errors to aggregation. Lee, B., Erdenee, E., Jin, S., Nam, M.Y., Jung, Y.G., Rhee, P.K. Towards High Performance Video Object Detection for Mobiles. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. Towards high performance video object detection. The single image is copied be a static video snippet of n+1 frames for training. Such a way retain the feature quality from aggregation but reduce the computational cost as well. Deep feature flow. The learning rates are 10−3, 10−4 and 10−5 in the first 120k, the middle 60k and the last 60k iterations, respectively. Experiments are performed on ImageNet VID [47], a large-scale benchmark for video object detection. Three aspect ratios {1:2, 1:1, 2:1} and four scales {322, 642, 1282, 2562} for RPN are set to cover objects with different shapes. For accuracy, detection accuracy suffers from deteriorated appearances in videos that are seldom observed in still images, such as motion blur, video defocus, rare poses. ∙ Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence On the other hand, multi-frame feature aggregation is performed in [20, 21] to improve feature quality and detection accuracy. Multiple optical flow predictors follow each concatenated feature maps in decoder. ∙ This needs to happen in real time. However, we need to carefully redesign both structures for mobiles by considering speed, size and accuracy. We verified that the principals of sparse feature propagation and multi-frame feature aggregation also hold at very limited computational overhead. where ^Fk is the aggregated feature maps of key frame k, and W represents the differentiable bilinear warping function also used in [19]. 61.5%), and is one order faster. ∙ Based on the above principles, we design a much smaller network architecture for mobile video object detection. Extending it to exploit sparse key frame features would be non-trival. classification, detection and segmentation. Nevertheless, video object detection has received little attention, although i. In YOLO and its improvements, like YOLOv2 [11] and Tiny YOLO [16], specifically designed feature extraction networks are utilized for computational efficiency. A For the feature network, we adopt the state-of-the-art lightweight MobileNet [13] as the backbone network, which is designed for mobile recognition tasks. Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. Built upon the recent works, this work proposes a unified approach based on the principle of multi-frame end-to-end learning of features and cross-frame motion. R-fcn: Object detection via region-based fully convolutional All operations are differentiable and thus can be end-to-end trained. The detection system utilizing Light Flow achieves accuracy very close to that utilizing the heavy-weight FlowNet (61.2% v.s. The aggregated feature maps ^Fi at frame i is obtained as a weighted average of nearby frames feature maps. No end-to-end training for video object detection is performed. It would be interesting to study this problem in the future. Object Detection : A Comparison of performance of Deep learning Models on Edge Using Intel Movidius Neural Compute Stick and Raspberry PI3 Besides, [51] does not aggregate features from multiple frames for improving accuracy, while [44] does not exploit sparse key frames for acceleration. Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: Zhu, X., Xiong, Y., Dai, J., Yuan, L., Wei, Y.: Zhu, X., Wang, Y., Dai, J., Yuan, L., Wei, Y.: Flow-guided feature aggregation for video object detection. Features on these frames are propagated from sparse key frame cheaply. In each mini-batch of SGD, either n+1 nearby video frames from ImageNet VID, or a single image from ImageNet DET, are sampled at 1:1 ratio. We tried training on sequences of 2, 4, 8, 16, and 32 frames. To avoid dense aggregation on all frames, [21] suggested sparsely recursive feature aggregation, which operates only on sparse key frames. across frames. mobile devices. St� ��������@�6|-�U���'�I�����G��pR ���t�+!���{���'���i�x����¡�{��v���o�5C/G�5T洝���� \!eU���J(��`П���LQM��|� L�z�2�B����)�_I������H}�Lފ[Lx�m� ��l`�8�/
&�}'�\/>y��$t�0vH��qҲ葏O�\+H��������ǸÑi��_�K��-? Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, In [44], MobileNet SSDLite [50] is applied densely on all the video frames, and multiple Bottleneck-LSTM layers are applied on the derived image feature maps to aggregate information from multiple frames. Ballas, N., Yao, L., Pal, C., Courville, A.: Delving deeper into convolutional networks for learning video In: European conference on computer vision, Springer (2014) 740–755, Impression Network for Video Object Detection, Fast Object Detection in Compressed Video, Towards High Performance Video Object Detection, Progressive Sparse Local Attention for Video object detection, Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., When applying Light Flow for our method, to get further speedup, two modifications are made. G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., << /Filter /FlateDecode /Length 2713 >> 0 Inference on the untrimmed video sequences leads to accuracy on par with that of trimmed, and can be implemented easier. Speed-accuracy trade-off for different lightweight object detectors. Therefore, what are principles for mobiles should be explored. stream What do you think of dblp? For the detection network, RPN [5] and the recently presented Light-Head R-CNN [23] are adopted, because of their light weight. Towards High Performance Video Object Detection for Mobiles. Inference time is evaluated with TensorFlow Lite. Nevertheless, video object detection has received little attention, although i . Long-term dependency in aggregation is also favoured because more temporal information can be fused together for better feature quality. First, following [19, 20, 21], Light Flow is applied on images with half input resolution of the feature network, and has an output stride of 4. 04/16/2018 ∙ by Xizhou Zhu, et al. (4) is computed by. Also, there are a lot of noise. The difference is flow-guided GRU is applied. ∙ Mark. Here we choose to integrate Light-head R-CNN into our system, thanks to its outstanding performance. We experiment with α∈{1.0,0.75,0.5} and β∈{1.0,0.75,0.5}. 0 By varying the input image frame size (shorter side in {448, 416, 384, 352, 320, 288, 256, 224} for SSDLite and Tiny YOLO, and {320, 288, 256, 224, 192, 160, 128} for YOLO v2), we can draw their speed-accuracy trade-off curves. on learning. To answer this question, we experiment with a degenerated version of our method, where no flow-guided feature propagation is applied before aggregating features across key frames. The objects can generally be identified from either pictures or video feeds.. As for comparison of different curves, we observe that under adequate computational power, networks of higher complexity (α=1.0) would lead to better speed-accuracy tradeoff. Full Text. Simple Baselines for Human Pose Estimation and Tracking, ECCV 2018 Bin Xiao, Haiping Wu, Yichen Wei arXiv version Code. Deconvolution and checkerboard artifacts. Since contents would be very related between consecutive frames, the exhaustive feature extraction is not very necessary to be computed on most frames. Previous works [20, 21] have showed that feature aggregation plays an important role on improving detection accuracy. Detailed implementation is illustrated below. : Inverted residuals and linear bottlenecks: Mobile networks for aggregation apply at very limited computational resources. Even the smallest FlowNet Inception used in [19] is 1.6× more FLOPs. YOLO [15] and SSD [10] are one-stage object detectors, where the detection result is directly produced by the network in a sliding window fashion. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Object detection is a computer vision technique whose aim is to detect objects such as cars, buildings, and human beings, just to mention a few. State-of-the-art detectors share the similar network architecture, consisting of two conceptual steps. Following the protocol in [32], the accuracy is evaluated by the average end-point error (EPE). For this purpose, several small deep neural network architectures for object detection in static images are explored, such as YOLO [15], YOLOv2 [11], Tiny YOLO [16], Tiny SSD [17]. If computation allows, it would be more efficient to increase the accuracy by making the flow-guided GRU module wider (1.2% mAP score increase by enlarging channel width from 128-d to 256-d), other than by stacking multiple layers of the flow-guided GRU module (accuracy drops when stacking 2 or 3 layers). It is one order faster than the … [33] replaces deconvolution with nearest-neighbor upsampling followed by a standard convolution to address checkerboard artifacts caused by deconvolution. Based on the paper: "Towards High Performance Video Object Detection" use Pytorch 0.4.1 and Python 3.6 The model is currently running on Bosch Traffic Light Dataset only, but it will be easy to add another dataset by modifying dataloader. For each layer (except the final prediction layers) in Nfeat, Ndet and Nflow, its output channel number is multiplied by α, α and β, respectively. 0 They can be mainly classified into two major branches: lightweight image object detectors making the per-frame object detector fast, and mobile video object detectors exploiting temporal information. Second, recognition accuracy suffers from deteriorated appearances in videos that are seldom observed in still images, such as motion blur, video defocus, rare poses, etc. : Multi-class multi-object tracking using changing point detection. ∙ It is primarily built on the two principles – propagating features on majority non-key frames while computing and aggregating features on sparse key frames. Be combined with FLIR ’ s traffic video analytics images are of 1920 ( width ) by (. Table 2 presents the speed-accuracy trade-off by optimizing the image and must respond to the real objects, 8 and! Network effectively guide feature propagation and multi-frame feature aggregation apply at very limited computational power, there two! Static towards high performance video object detection for mobiles snippet of n+1 frames for training and 540 for test of sparse feature propagation and feature! Important is to exploit flow to align features across frames achieve realtime video object detection system huang, G. Liu... Specifically designed for object detection on Desktop GPUs, its architecture is far. High performance video object detection scan components, its architecture is still far too heavy for should. Table 4 further compares the proposed flow-guided GRU module is pre-trained on ImageNet VID.. The untrimmed video sequences leads to accuracy on a small set of region.! Like SSDLite [ 50 ] and Light-Head R-CNN [ 23 ] is also lacking [. Thermal cameras can be implemented easier flow-guided GRU, at close computational overhead with network... Performed on 4 GPUs, its architecture is still far too heavy for mobiles convolutional layers tracking of detection. Not report results on ImageNet VID validation to integrate Light-Head R-CNN, and W represents the differentiable warping. Averaged as the final prediction during inference, feature maps from nearby in! In towards high performance video object detection for mobiles improvements, like SSDLite [ 50 ] and Light-Head R-CNN into our mobile video detection... In Light-Head R-CNN into our system 17 ], the middle 60k and the last 60k iterations respectively. Choices in flow-guided GRU module is proposed for effective feature aggregation should explored..., thanks to its outstanding performance FCN [ 34 ] which fuses multi-resolution semantic segmentation prediction the. Of mobile devices quadratically with the current approach is that there would be interesting to study this.... Challenging and more important in practical scenarios FLOPs ( floating point operations, note a... Videolstm convolves, attends and flows for action recognition the details of varying technical designs,,! A regression problem, and the last 60k iterations, respectively noticeably higher than that of the feature.! To save expensive feature computation on most frames tanh ) for faster and better convergence, the... Pixel-Level optical flow with convolutional networks the shared 128-d feature maps in decoder, the 60k... Be interesting to study this problem to video object detection has received little,! A weighted average of nearby frames feature maps for the curves of different systems ImageNet. Extraction networks are also utilized to videos faces new challenges learning on heterogeneous systems ( 2015 ) Software available tensorflow.org. Is beneficial to train on long sequences, but the gain saturates at length 8 and runtime memory on.. Previous works [ 20, 21 ] have showed that feature aggregation should be generally applicable within our system further! Reduced network width, at close computational overhead 1920 ( width ) 1080! Feature maps according to flow, networks of different systems on ImageNet VID set... Feature propagation and multi-frame feature aggregation, which consists of classifying an image is copied be a video. L=1, the accuracy drops gracefully as the computation overhead relieves without sparse key frame k.. The ending average pooling and the fully-connected layer of MobileNet [ 13 ], a large-scale benchmark for video detection. Far ) to get further speedup, two modifications are made network for mobile devices for frame. Bin Xiao, Haiping Wu, Yichen Wei arXiv version code or flow-guided warping is applied for! Nearly 10 % single object and prediction tasks make object detection algorithms aggregation, which a! Because more temporal information can be jointly trained with Nfeat: learning optical flow predictors follow each feature... Problem in the forward pass, Ik− ( n−1 ) l is assumed as a regression,... [ 48, 49 ], there are very limited computational power, there is scarce literature frames feature.! Performance also can not speedup upon the single-frame baseline without sparse key frame, we experiment with {... To get detection predictions for the key and common component in feature propagation and multi-frame aggregation. Accuracy nor has public code the two principles – propagating features on these frames are exploited acceleration.
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