Multi-Camera Collaborative Network Experimental Study Design of Video Surveillance System for Violated Vehicles Identification

Abstract

In this paper, we propose an experimental study of multi-camera collaborative network for surveillance the highway traffic turn in real life scenario, the target of surveillance based offline video processing is capturing the violated vehicles that driving violated paths in many cases specified by user-defined rules. Best topology of the experiment zone is considered and covered by four pillars; each has two (fixed and motorized) cameras that casing the entire specific effective field of view. As to author knowledge, there is no such available experiment, and hence, it could serve researchers that interested in. However, the experiment is done for around 180 recorded hours for 8 videos during 9 days, each video for one camera. It is designed based on the collaborative cameras principle for intelligent video surveillance systems and the outcomes show that the surveillance and the tracking of violated vehicles have been successes in most user-defined rules cases for more than 90 % cases.

Threshold adaptation and XOR accumulation algorithm for objects detection

“…In [28], a speed computation algorithm of object detection is proposed which first removes noisy pixels and then applies some adaptive thresholds to catch moving objects as a foreground extraction. However, based on [29]- [33], even they propose new techniques but the problem of separating noisy background pixels in an outdoor environment remains present. The proposed threshold adaptation and XOR accumulation (TAXA) algorithm works based on how much information is available that surrounds each pixel; this information can decide whether the pixel belongs to the foreground or background; the decision is made according to the novelty use of XOR-theory for crucial adaptive thresholds of statistic techniques as shown in section 3 with detail.…”