Computer scientists have made ceaseless efforts to replicate cognitive video understanding abilities of human brains onto autonomous vision systems. As video surveillance cameras become ubiquitous, there is a surge in studies on automated activity understanding and unusual event detection in surveillance videos. Nevertheless, video content analysis in public scenes remained a formidable challen...

The enabling technologies of increasing PC bus bandwidth, multicore processors, and advanced graphics processors combined with a high-performance multi-image camera system are leading to new ways of considering video. We describe scalable varied-resolution video capture, presenting a novel method of generating multi-resolution dialable-shape panoramas, a line-based calibration method that achie...

With the increase of the number of cameras installed across a video surveillance network, the ability of security staffs to attentively scan all the video feeds actually decreases. Therefore, the need for an intelligent system that operates as a tracking system is vital for security personnel to do their jobs well. Tracking people as they move through a camera network with non-overlapping field...

Conventional tracking approaches assume proximity in space, time and appearance of objects in successive observations. However, observations of objects are often widely separated in time and space when viewed from multiple non-overlapping cameras. To address this problem, we present a novel approach for establishing object correspondence across non-overlapping cameras. Our multi-camera tracking...

Solving the person re-identification problem involves matching observations of individuals across disjoint camera views. The problem becomes particularly hard in a busy public scene as the number of possible matches is very high. This is further compounded by significant appearance changes due to varying lighting conditions, viewing angles and body poses across camera views. To address this pro...

In recent years, a growing body of research has focused on the problem of person re-identification (re-id). The re-id techniques attempt to match the images of pedestrians from disjoint non-overlapping camera views. A major challenge of re-id is the serious intra-class variations caused by changing viewpoints. To overcome this challenge, we propose a deep neural network-based framework which ut...

Person re-identification (re-id) refers to matching pedestrians across disjoint yet non-overlapping camera views. The most effective way to match these pedestrians undertaking significant visual variations is to seek reliably invariant features that can describe the person of interest faithfully. Most of existing methods are presented in a supervised manner to produce discriminative features by...

The specific problem we address in this chapter is object tracking over wide-areas such as an airport, the downtown of a large city or any large public area. Surveillance over these areas consists of the search for suspicious behavior such as persons loitering, unauthorized access, or persons attempting to enter a restricted zone. Until now, these surveillance tasks have been executed by human ...

Illumination differences between disjoint cameras can have a dramatic effect on the appearance of objects, thus increasing the difficulty of multi-camera object association. Although methods to model these inter-camera illumination conditions exist, they often rely on static illumination conditions and are unable to copewith unpredictable illumination changes over time. In this paper we propose...

With the increase of the number of cameras installed across a video surveillance network, the ability of security staffs to attentively scan all the video feeds actually decreases. Therefore, the need for an intelligent system that operates as a tracking system is vital for security personnel to do their jobs well. Tracking people as they move through a camera network with non-overlapping field...