Region Trajectories for Video Semantic Concept Detection.

Recently, with the advent of the convolutional neural network (CNN), many CNN-based object detection algorithms have been proposed and achieved encouraging results. In this paper, we introduce an algorithm based on region trajectories to establish the connections between object localizations in individual frames and video sequences. To detect object regions in the individual frames of a video, we enhance the region-based convolutional neural network (R-CNN), by incorporating EdgeBox with the Selective Search to generate candidate region proposals and combining the GoogLeNet with the AlexNet to improve the discriminability of the feature representations. The DeepMatching algorithm is employed in our proposed region trajectory method to track the points in the detected object regions. The experiments are conducted on the validation split of the TRECVID 2015 Localization dataset. As demonstrated by the experimental results, our proposed approach improves the object detection accuracy in both temporal and spatial measurements.