Intelligent traffic systems for traffic surveillance and monitoring have become a topic of great
interest to some cities in the world. Generally, the existing traffic surveillance systems are …

In this paper, a self-learning approach is proposed towards solving scene-specific
pedestrian detection problem without any human'annotation involved. The self-learning …

In a semi-supervised setting, direct training of a deep discriminative model on partially
labeled images often suffers from overfitting and poor performance, because only a small …

Generally, the performance of a generic detector decreases significantly when it is tested on
a specific scene due to the large variation between the source training dataset and the …

Vision based pedestrian tracking becomes a hard problem when long-term/heavy occlusion
happens or pedestrian temporarily moves out of the visual field. In this paper, a novel …

You only look once (YOLO) is one of the most efficient target detection networks. However,
the performance of the YOLO network decreases significantly when the variation between …

The performance of the learning-based detector depends much on its training dataset and
decreases rapidly when it is tested on a new scene. The reason is that in the large variations …

The computer vision community has developed many multi-object tracking methods in
various fields. The focus is put on traffic scenes and video-surveillance applications where …

We propose a method for specializing deep detectors and trackers to restricted settings. Our
approach is designed with the following goals in mind:(a) Improving accuracy in restricted …

In this work, we propose a novel approach to automatically specialize a generic pedestrian
detector to specific scene by utilizing the sequential Monte Carlo filter and the Faster R-CNN …