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.
Keywords: Classification of vehicles, urban traffic, detection of
vehicles, Neural Networks, dense SIFT