Despite the recent progress in both pixel-domain and compressed-domain video object
tracking, the need for a tracking framework with both reasonable accuracy and reasonable …

Video surveillance is one of the widely used and most active research applications of
computer vision. Although lots of works have been done in the area of smart surveillance …

This paper presents a spatio-temporal graph-based method of detecting and tracking
moving objects by treating the encoded blocks with non-zero motion vectors and/or non-zero …

Many edge applications rely on expensive Deep-Neural-Network (DNN) inference-based
video analytics. Typically, a single instance of an inference service analyzes multiple …

We present MVmed, an algorithm for real-time online tracking of people and objects in
MPEG-4 and H. 264 compressed videos and integrate it into a multi-purpose tracking …

In this paper we propose a hybrid tracking method which detects moving objects in videos
compressed according to H. 265/HEVC standard. Our framework largely depends on motion …

We present a novel system to perform efficient, compressed domain aided video analysis in
the context of traffic surveillance applications. After camera installation, the system initializes …

In this paper we present a compressed-domain object tracking algorithm for H. 264/AVC
compressed videos and integrate the proposed algorithm into an indoor vehicle tracking …

Motion detection and tracking is an important vision topic for many applications such as
video surveillance. When this process takes place during video encoding and transmission …

In this paper we present a novel method to detect and track moving objects in H. 264/AVC
bitstreams by processing motion vector and residue information. The encoded blocks with …