Video Object Detection Using Event-Aware Convolutional Lstm and Object Relation Networks - Chen Zhang (Electronics, 2021)
Common video-based object detectors exploit temporal contextual information to improve the performance of object detection. However, detecting objects under challenging conditions has not been thoroughly studied yet. In this paper, we focus on improving the detection performance for challenging events such as aspect ratio change, occlusion, or large motion. To this end, we propose a video object detection network using event-aware ConvLSTM and object relation networks. Our proposed event-aware ConvLSTM is able to highlight the area where those challenging events take place. Compared with traditional ConvLSTM, with the proposed method it is easier to exploit temporal contextual information to support video-based object detectors under challenging events. To further improve the detection performance, an object relation module using supporting frame selection is applied to enhance the pooled features for target ROI. It effectively selects the features of the same object from one of the reference frames rather than all of them. Experimental results on ImageNet VID dataset show that the proposed method achieves mAP of 81.0% without any post processing and can handle challenging events efficiently in video object detection.