In the multi-object tracking process, a long-term tracking algorithm for traffic scene based on
deep learning is proposed to handle several challenging problems, such as the complex …

Recently, with the development of deep-learning, the performance of multi-object tracking
algorithms based on deep neural networks has been greatly improved. However, most …

Object appearance representation is crucial to any visual tracker. In order to improve the
appearance representation, we propose a deep compact and high-level appearance …

Using Convolutional Neural Networks (CNN) in object tracking typically utilizes spatial
features, while ignores the temporal correlation of frames in the whole film, causing that it is …

In this paper, we extend the discriminant correlation filter (DCF) based deep learning tracker
to multi-object tracking. For each object, we use an individual tracker to estimate the …

In recent years, object detection and data association have getting remarkable progress
which are the core components for multi-object tracking. In multi-object tracking field, the …

Multiple object tracking suffers from many challenges including huge computation work,
crowd scenes. In order to solve these problems, we proposed a novel online multiple object …

Multi-object tracking is a research hotspot in intelligent driving scenarios, and the trajectory
association of tracked objects is one of the hotspots that needs to be solved urgently. The …

Multi-target tracking technology can achieve real-time tracking by detecting the identity
information of multiple targets. It is a key technology in the field of computer vision and has …

Multi-object tracking is an important but challenging task in computer vision. Tremendous
investigations have been made on the topics, among which tracking-by-detection method …