Investigations into particle swarm optimization for multi-class shape recognition

There has been a significant drop in the cost as well as an increase in the quality of imaging sensors due to stiff competition as well as production improvements. Consequently, real-time surveillance of private or public spaces which relies on such equipment is gaining wider acceptance. While the human brain is very good at image analysis, fatigue and boredom may contribute to a less-than-optimum level of monitoring performance. Clearly, it would be good if highly accurate vision systems could complement the role of humans in round-the-clock video surveillance. This paper addresses an image analysis problem for video surveillance based on the particle swarm computing paradigm. In this study three separate datasets were used. The overall finding of the paper suggests that clustering using Particle Swarm Optimization leads to better and more consistent results, in terms of both cluster characteristics and subsequent recognition, as compared to traditional techniques such as K-Means.