Comparative analysis of vehicle detection in urban traffic environment using Haar cascaded classifiers and blob statistics

The applications of computer vision are widely used in traffic monitoring and surveillance. In traffic monitoring, detection of vehicles plays a significant role. Different attributes such as shape, color, size, pose, illumination, shadows, occlusion, background clutter, camera viewing angle, speed of vehicles and environmental conditions pose immense and varying challenges in the detection phase. The native urban datasets namely NIPA and TOLL PLAZA acquired in complex traffic environment are used for research analysis. The selected datasets include varying attributes highlighted above. The NIPA dataset has total of 1516 vehicles whereas the TOLL PLAZA dataset contains 376 vehicles in an entire video sequence. This paper provides comparative analysis and insight on performance of cascade of boosted classifier using Haar features versus statistical analysis using blobs. Haar features help effectively in extracting discernible regions of interest in complex traffic scenes and has minimum false detection rate as compared to blob analysis. The detection results obtained from the trained Haar cascade classifier for NIPA and TOLL PLAZA datasets have 83.7% and 88.3% accuracy respectively. In contrast blob analysis has detection accuracy of only 43.8% for NIPA and 65.7% for TOLL PLAZA datasets.