Object detection in videos has drawn increasing attention recently with the introduction of the large-scale ImageNet VID dataset. Different from object detection in static images, temporal information in videos provides vital information for object detection. To fully utilize temporal information, state-of-the-art methods [14, 13] are therefore based on spatiotemporal tubelets, which are essentially sequences of associated bounding boxes across time. However, the existing methods have major limitations in generating tubelets in terms of quality and efficiency. Motionbased [13] methods are able to obtain dense tubelets, but the lengths are generally only several frames, which is not optimal to incorporate long-term temporal information. Appearance-based [14] methods, usually involving generic object tracking, could generate long tubelets, but are usually computational expensive. In this work, we propose a framework for object detection in videos, which consists of a novel tubelet proposal network to efficiently generate spatiotemporal proposals, and a Long Short-term Memory (LSTM) network that incorporates temporal information from tubelet proposals for achieving high object detection accuracy in videos. The experiments on the large-scale ImageNet VID dataset demonstrate the effectiveness of the proposed framework for object detection in videos.
Tpn REF proposes a tubelet proposal network and employs an LSTM to incorporate tem-poral information from tubelet proposals.
Object Detection in Videos with Tubelet Proposal Networks
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science