Pedestrian detection and tracking using particle filtering

Pedestrian detection is one of the significant task in any intelligent systems involving video surveillance, since it provides essential information regarding the semantic behavior of pedestrians from video footages. Pedestrian detection along with tracking serves as an obvious extension to automotive applications in design and improvement of safety systems. However, various challenges arise while designing a system for detection and tracking of pedestrians such as different styles of clothing, non-linear random motion of pedestrians, occlusions between pedestrians and surroundings. Particle filtering algorithm is best suited to overcome these types of difficulties. Existing approaches such as Kalman filtering technique are also being implemented in this paper in order to compare the results and further prove that the Particle filtering dominates over the existing approaches.