Detection and classification of vehicles is a paramount task in surveillance framework and for traffic management and control. The type of transportation infrastructure, road conditions, traffic trends and illumination conditions are some of the key factors that affect these essential
tasks. This paper explores performance of existing techniques regarding detection and classification in local, day time, complex urban traffic videos with increased free flowing vehicle volume. Three different traffic datasets with varying level of complexity are used for analysis. The scene
complexity is governed by factors such as vehicle speed, type and size of dynamic objects, direction of motion of vehicles, number of lanes, occlusion, length and camera viewing angle. The datasets include a big classification volume ranging to 1516 vehicles in NIPA (customized local dataset)
and 1009 vehicles in TOLL PLAZA (customized local dataset) along-with a publicly available dataset with 51 vehicles namely, HIGHWAY II. Existing detection algorithms such as blob analysis, Kalman filter tracking and detection lines were applied for detection on all the three datasets and experimental
results are presented. Results show that the algorithms perform well for low density, low speed, less shadow, better image resolution, appropriate camera viewing angle, better lighting conditions and occlusion free zones. However, as soon as the complexity of the scene is increased, several
detection errors are identified. Further obtaining robust and invariant features of local vehicles design has been challenging during the process. A custom GUI is built to analyze results of the algorithm. This detection is further extended to classification of 231 vehicles of NIPA dataset
which is a highly complex urban traffic scenario. Vehicles are classified as Small Vehicle (SV), Large Vehicle (LV) and Motorcycle (M) by using area threshold based classifier and dense Scale Invariant Feature Transform (SIFT) and Artificial Neural Network (ANN) classifier. Detailed comparison
of both classifier results show that SIFT and ANN classifier performs better for classification tasks in highly complex urban scenarios and also points out that practical systems still require a robust classification scheme to get more than 80% accuracy.