Abstract
Zoom tracking is an important function in video surveillance, particularly in traffic management and security monitoring. It involves keeping an object of interest in focus during the zoom operation. Zoom tracking is typically achieved by moving the zoom and focus motors in lenses following the so-called “trace curve”, which shows the in-focus motor positions versus the zoom motor positions for a specific object distance. The main task of a zoom tracking approach is to accurately estimate the trace curve for the specified object. Because a proportional integral derivative (PID) controller has historically been considered to be the best controller in the absence of knowledge of the underlying process and its high-quality performance in motor control, in this paper, we propose a novel feedback zoom tracking (FZT) approach based on the geometric trace curve estimation and PID feedback controller. The performance of this approach is compared with existing zoom tracking methods in digital video surveillance. The real-time implementation results obtained on an actual digital video platform indicate that the developed FZT approach not only solves the traditional one-to-many mapping problem without pre-training but also improves the robustness for tracking moving or switching objects which is the key challenge in video surveillance.
Highlights
Due to the remarkable growth in the video surveillance market over the last few years [1,2,3], high-quality imaging results from zoom operation are demanded by consumers [4,5], in traffic management and security monitoring [6,7,8]
The existing zoom tracking methods can be divided into two categories: (1) geometric methods, such as geometric zoom tracking (GZT) and adaptive zoom tracking (AZT); (2) machine learning methods, such as relational zoom tracking (RZT) and predictive zoom tracking (PZT)
To track moving and switching objects in digital video surveillance and to acquire better estimated results without pre-training the system, we propose the robust feedback zoom tracking (FZT) method to revise the estimated trace curve, which is based on traditional GZT estimation and utilises a proportional-integral-derivative (PID) loop-closed feedback controller [20,21,22]
Summary
Due to the remarkable growth in the video surveillance market over the last few years [1,2,3], high-quality imaging results from zoom operation are demanded by consumers [4,5], in traffic management and security monitoring [6,7,8]. Maintaining image sharpness or focus during the entire zoom process is the main challenge of zoom tracking. The plant remains in-focus as the zoom is changed by the user in the presence of zoom tracking. The image becomes out-of-focus in the absence of zoom tracking, and the image clarifies after zoom tracking due to an auto-focusing (AF) [9] algorithm
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