Vehicle Detection and Tracking Based on Color Feature
Mallikarjun Anandhalli, Vishwanath P. Baligar · 2017
Determining the vehicle density running on the specific road is an important topic in Intelligent Transportation System. Reliable and robust vehicle detection from video sequence is an important problem in the application to control the traffic. The processing time of the vehicle detection system should meet the real time and should be free from false detection. The density of the vehicles defines the usage of road, traffic signals, etc. If the congestion is high road widening should be taken place or the vehicle should be diverted to the other way so that congestion cannot take place the next time. The algorithm proposed in the system detects vehicle and track them in real time. Detection of the vehicles is purely carried on color features of the vehicles. After detection of the vehicles they are tracked using enhanced Kalman filter with the data association. Cost matrix for data association plays an important role in allocating a centroid to each enhanced Kalman filter. So, that Kalman filter tracks the same centroid. The number of vehicles running in a video or in particular lane can be determined. Detection, tracking, convex hull, connected component labeling, color extraction.