Fast Prototyping for Video Monitoring Systems with the Use of DSP Module

This paper presents techniques for fast prototyping of real-time hardware / software video processing systems for urban surveillance monitoring equipment. During the experimental research the evaluation module with the TMS320DM6437 signal processor programmed with the use of the Code Composer Studio and Matlab / Simulink environments has been used. Analyzed algorithms can support the work of monitoring video operators. In particular, we analyzed efficiency of implementation of the algorithms using two examples: detection of painting theft and signaling of crossing a pedestrian pass at the red light

Robust visual behavior recognition

In this article, we propose a novel framework for robust visual behavior understanding, capable of achieving high recognition rates in demanding real-life environments and in almost real time. Our approach is based on the utilization of holistic visual behavior understanding methods, which perform modeling directly at the pixel level. This way, we eliminate the world representation layer that can be a significant source of errors for the modeling algorithms. Our proposed system is based on the utilization of information from multiple cameras, aiming to alleviate the effects of occlusions and other similar artifacts, which are rather common in real-life installations. To effectively exploit the acquired information for the purpose of real-time activity recognition, appropriate methodologies for modeling of sequential data stemming from multiple sources are examined. Moreover, we explore the efficacy of the additional application of semisupervised learning methodologies, in an effort to reduce the cost of model training in a completely supervised fashion. The performance of the examined approaches is thoroughly evaluated under real-life visual behavior understanding scenarios, and the obtained results are compared and discusse