Performance analysis of a surveillance system to detect and track vehicles using Haar cascaded classifiers and optical flow method
Kazy Noor, Kazy Noor e Alam Siddiquee, Dhiman Sarma, Avijit Nandi, Sharmin Akhter, Sohrab Hossain, Karl Andersson, Mohammad Shahadat Hossain · 2017
This paper presents the real time vehicle detection and tracking system, based on data, collected from a single camera. In this system, vehicles are detected by using Haar Feature-based Cascaded Classifier on static images, extracted from the video file. The advantage of this classifier is that, it uses floating numbers in computations and hence, 20% more accuracy can be achieved in comparison to other classifiers and features of classifiers such as LBP (Local Binary Pattern). Tracking of the vehicles is carried out using Lucas-Kanade and Horn Schunk Optical Flow method because it performs better than other methods such as Morphological and Correlation Transformations. The proposed system consists of vehicle detection and tracking; and it is evaluated by using real data, collected from the route networks of Chittagong City of Bangladesh.