Object Detection in Video with Spatial-temporal Context Aggregation
This work addresses video object detection for computer vision applications, offering a novel method to bypass feature correspondence issues, though it is incremental in building on existing proposal-level techniques.
The paper tackles the problem of unstable feature correspondence in video object detection by proposing a feature aggregation framework that models semantic and spatio-temporal relationships among object proposals, achieving 80.3% mAP on ImageNet VID and improving the baseline by 5.8% mAP.
Recent cutting-edge feature aggregation paradigms for video object detection rely on inferring feature correspondence. The feature correspondence estimation problem is fundamentally difficult due to poor image quality, motion blur, etc, and the results of feature correspondence estimation are unstable. To avoid the problem, we propose a simple but effective feature aggregation framework which operates on the object proposal-level. It learns to enhance each proposal's feature via modeling semantic and spatio-temporal relationships among object proposals from both within a frame and across adjacent frames. Experiments are carried out on the ImageNet VID dataset. Without any bells and whistles, our method obtains 80.3\% mAP on the ImageNet VID dataset, which is superior over the previous state-of-the-arts. The proposed feature aggregation mechanism improves the single frame Faster RCNN baseline by 5.8% mAP. Besides, under the setting of no temporal post-processing, our method outperforms the previous state-of-the-art by 1.4% mAP.