Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector

Multiple object tracking (MOT) in urban traffic aims to produce the trajectories of the different road users that move across the field of view with different directions and speeds and that can have varying appearances and sizes. Occlusions and interactions among the different objects are expected and common due to the nature of urban road traffic. In this work, a tracking framework employing classification label information from a deep learning detection approach is used for associating the different objects, in addition to object position and appearances. We want to investigate the performance of a modern multiclass object detector for the MOT task in traffic scenes. Results show that the object labels improve tracking performance, but that the output of object detectors are not always reliable.Comment: 13th International Symposium on Visual Computing (ISVC

Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection

In this paper, we propose to combine detections from background subtraction and from a multiclass object detector for multiple object tracking (MOT) in urban traffic scenes. These objects are associated across frames using spatial, colour and class label information, and trajectory prediction is evaluated to yield the final MOT outputs. The proposed method was tested on the Urban tracker dataset and shows competitive performances compared to state-of-the-art approaches. Results show that the integration of different detection inputs remains a challenging task that greatly affects the MOT performance

The Caltech-UCSD Birds-200-2011 Dataset

CUB-200-2011 is an extended version of CUB-200 [7], a challenging dataset of 200 bird species. The extended version roughly doubles the number of images per category and adds new part localization annotations. All images are annotated with bounding boxes, part locations, and at- tribute labels. Images and annotations were filtered by mul- tiple users of Mechanical Turk. We introduce benchmarks and baseline experiments for multi-class categorization and part localization