Paper Info
Title
Single Shot Video Object Detector
Abstract
Single shot detectors that are potentially faster and simpler than two-stage detectors tend to be more applicable to object detection in videos. Nevertheless, the extension of such object detectors from image to video is not trivial especially when appearance deterioration exists in videos, e.g., motion blur or occlusion. A valid question is how to explore temporal coherence across frames for boosting detection. In this paper, we propose to address the problem by enhancing per-frame features through aggregation of neighboring frames. Specifically, we present Single Shot Video Object Detector (SSVD) - a new architecture that novelly integrates feature aggregation into a one-stage detector for object detection in videos. Technically, SSVD takes Feature Pyramid Network (FPN) as backbone network to produce multi-scale features. Unlike the existing feature aggregation methods, SSVD, on one hand, estimates the motion and aggregates the nearby features along the motion path, and on the other, hallucinates features by directly sampling features from the adjacent frames in a two-stream structure. Extensive experiments are conducted on ImageNet VID dataset, and competitive results are reported when comparing to state-of-the-art approaches. More remarkably, for $448 \times 448$ input, SSVD achieves 79.2% mAP on ImageNet VID, by processing one frame in 85 ms on an Nvidia Titan X Pascal GPU. The code is available at https://github.com/ddjiajun/SSVD.
Year
DOI
Venue
2021
10.1109/TMM.2020.2990070
IEEE Transactions on Multimedia
Keywords
DocType
Volume
Video object detection,single shot detection,feature aggregation
Journal
23
ISSN
Citations 
PageRank 
1520-9210
3
0.37
References 
Authors
0
6
Name
Order
Citations
PageRank
Jiajun Deng163.86
Yingwei Pan235723.66
Ting Yao384252.62
Wengang Zhou4122679.31
Houqiang Li52090172.30
Tao Mei64702288.54