Vehicles Tracking by Combining Convolutional Neural Network Based Segmentation and Optical Flow Estimation
Résumé
Object tracking is an important proxy task towards action recognition. The recent successful CNN models for detection and segmentation, such as Faster R-CNN and Mask R-CNN lead to an effective approach for tracking problem: tracking-by-detection. This very fast type of tracker takes into account only the Intersection-Over-Union (IOU) between bounding boxes to match objects without any other visual information. In contrast, the lack of visual information of IOU tracker combined with the failure detections of CNNs detectors create fragmented trajectories. Inspired by the work of Luc et al that predicts future segmentations by using Optical flow, we propose an enhanced tracker based on tracking-by-detection and optical flow estimation in vehicle tracking scenario. Our solution generates new detections or segmentations based on translating backward and forward results of CNNs detectors by optical flow vectors. This task can fill in the gaps of trajectories. The qualitative results show that our solution achieved stable performance with different types of flow estimation methods. Then we match generated results with fragmented trajectories by SURF features. DAVIS dataset is used for evaluating the best way to generate new detections. Finally, the entire process is test on DETRAC dataset. The qualitative results show that our methods significantly improve the fragmented trajectories.