Abstract
The video surveillance, such as an example of security system presents one of the powerful techniques used in advanced systems. Manual vision which is used to analyze video in the traditional approach should be avoided. An automated surveillance system based on suspicious behavior presents a great challenge to developers. The detection is encountered by complexity and time-consuming process. An abnormal behavior could be identified by different ways: actions, face, trajectory, etc. The characteristics of an abnormal behavior still presents a great problem. This paper proposes a specific System On Chip architecture for surveillance system based on Multi-Processor (MPSOC) and hardware accelerator. The aim is to accelerate the processing and obtain a reliable and accelerated suspicious behavior recognition. Finally, the experiment section proves the opportunity of the proposed system in terms of performance and cost.
Highlights
Nowadays, our lifetime is widely conditioned by different surveillance systems
The implementation of the surveillance system using a hybrid architecture based on multi-processor and Support Vector Machine (SVM) based on a hardware accelerator is discussed
The purpose of this work is to obtain a real-time execution of the surveillance system using two NIOS II processors, hardware accelerator, and distributed memory
Summary
Our lifetime is widely conditioned by different surveillance systems. All of them are increasingly monitored by computers. The main goal of surveillance system is identifying suspicious or undesirable behaviors such as thefts and looting with intent [1]. An abnormal action or behavior represents a suspicious behavior which could menace human life by different way as freedom, privacy, health, and properties [2]. Developers propose three essential steps (see Fig. 1) to recognize the suspicious behavior: object detection, tracking, and behavior exploration. The first challenge was to define models to recognize a suspicious behavior. A suspicious behavior did not have a standard pattern and the recognition phase is challenged by the accuracy of the abnormal detection. They aimed to find an automated method to analyze suspicious behavior and to replace traditional monitors
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