Video Object Detection with Locally-Weighted Deformable Neighbors

Authors

  • Zhengkai Jiang Chinese Academy of Sciences
  • Peng Gao Chinese University of Hong Kong
  • Chaoxu Guo Chinese Academy of Sciences
  • Qian Zhang Horizon Robotics
  • Shiming Xiang Chinese Academy of Sciences
  • Chunhong Pan Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v33i01.33018529

Abstract

Deep convolutional neural networks have achieved great success on various image recognition tasks. However, it is nontrivial to transfer the existing networks to video due to the fact that most of them are developed for static image. Frame-byframe processing is suboptimal because temporal information that is vital for video understanding is totally abandoned. Furthermore, frame-by-frame processing is slow and inefficient, which can hinder the practical usage. In this paper, we propose LWDN (Locally-Weighted Deformable Neighbors) for video object detection without utilizing time-consuming optical flow extraction networks. LWDN can latently align the high-level features between keyframes and keyframes or nonkeyframes. Inspired by (Zhu et al. 2017a) and (Hetang et al. 2017) who propose to aggregate features between keyframes and keyframes, we adopt brain-inspired memory mechanism to propagate and update the memory feature from keyframes to keyframes. We call this process Memory-Guided Propagation. With such a memory mechanism, the discriminative ability of features in keyframes and non-keyframes are both enhanced, which helps to improve the detection accuracy. Extensive experiments on VID dataset demonstrate that our method achieves superior performance in a speed and accuracy trade-off, i.e., 76.3% on the challenging VID dataset while maintaining 20fps in speed on Titan X GPU.

Downloads

Published

2019-07-17

How to Cite

Jiang, Z., Gao, P., Guo, C., Zhang, Q., Xiang, S., & Pan, C. (2019). Video Object Detection with Locally-Weighted Deformable Neighbors. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 8529-8536. https://doi.org/10.1609/aaai.v33i01.33018529

Issue

Section

AAAI Technical Track: Vision