508 research outputs found
Detect or Track: Towards Cost-Effective Video Object Detection/Tracking
State-of-the-art object detectors and trackers are developing fast. Trackers
are in general more efficient than detectors but bear the risk of drifting. A
question is hence raised -- how to improve the accuracy of video object
detection/tracking by utilizing the existing detectors and trackers within a
given time budget? A baseline is frame skipping -- detecting every N-th frames
and tracking for the frames in between. This baseline, however, is suboptimal
since the detection frequency should depend on the tracking quality. To this
end, we propose a scheduler network, which determines to detect or track at a
certain frame, as a generalization of Siamese trackers. Although being
light-weight and simple in structure, the scheduler network is more effective
than the frame skipping baselines and flow-based approaches, as validated on
ImageNet VID dataset in video object detection/tracking.Comment: Accepted to AAAI 201
Quality-Gated Convolutional LSTM for Enhancing Compressed Video
The past decade has witnessed great success in applying deep learning to
enhance the quality of compressed video. However, the existing approaches aim
at quality enhancement on a single frame, or only using fixed neighboring
frames. Thus they fail to take full advantage of the inter-frame correlation in
the video. This paper proposes the Quality-Gated Convolutional Long Short-Term
Memory (QG-ConvLSTM) network with bi-directional recurrent structure to fully
exploit the advantageous information in a large range of frames. More
importantly, due to the obvious quality fluctuation among compressed frames,
higher quality frames can provide more useful information for other frames to
enhance quality. Therefore, we propose learning the "forget" and "input" gates
in the ConvLSTM cell from quality-related features. As such, the frames with
various quality contribute to the memory in ConvLSTM with different importance,
making the information of each frame reasonably and adequately used. Finally,
the experiments validate the effectiveness of our QG-ConvLSTM approach in
advancing the state-of-the-art quality enhancement of compressed video, and the
ablation study shows that our QG-ConvLSTM approach is learnt to make a
trade-off between quality and correlation when leveraging multi-frame
information. The project page: https://github.com/ryangchn/QG-ConvLSTM.git.Comment: Accepted to IEEE International Conference on Multimedia and Expo
(ICME) 201
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