No abstract

The intelligent traffic management system (ITS) is one of the active research areas. Vehicle detection is a major role in traffic analysis. In the paper, analysis of detecting vehicles is proposed based on the features posed by the vehicle. The foreground pixels from image are extracted by histogram based foreground segmentation. After segmenting, Hu-Moments and Eigen values features are extracted and normalized. The classifiers are trained with the extracted Hu-Moments and Eigen values. The experiments are conducted on different benchmark datasets, and results are analysed considering the overall classification accuracy. Results of the algorithm are satisfactory and acceptable in real time.

<span lang="EN-US">The detection of object with respect to Vehicle and tracking is the most needed step in computer research area as there is wide investment being made form Intelligent Traffic Management. Due to changes in vision or scenes, detection and tracking of vehicle under different drastic conditions has become most challenging process because of the illumination, shadows etc. To overcome this, we propose a method which uses TensorFlow fused with corner points of the vehicles for detection of vehicle and tracking of an vehicle is formulated again, the location of the object which is detected is passed to track the detected object, using the tracking algorithm based on CNN. The proposed algorithm gives result of 90.88% accuracy of detection in video sequences under different conditions of climate.</span>

No abstract

Recent developments in Information and Communication Technology have facilitated a new concept of Smart City to the world. The Smart City concept is driven by technology intervention in every aspect of city life, including the most dynamic and unpredictable transport management. Intelligent transport management system (ITMS) is the most essential component of a Smart City ecosystem. The primary function is to ensure smooth and accident-free transport on the city roads. ITMS can prompt drivers of possible traffic jams; ITMS can be used to detect speed violations of vehicles. One of the primary inputs to ITMS is fed from closed circuit television (CCTV) installations on the roads. The main objective of the paper is to detect vehicles violating traffic rules especially over-speeding. The detection of over-speeding of a vehicle involves detection of vehicle, calculation and calibration of the distance traveled by the vehicle both on an image plane and real world. To calibrate the distance traveled by the vehicle, the geometric plane of the real world is projected onto the image plane. The projection onto the image plane helps in determining the actual distance traveled by the vehicle in the real world. After calibration of the distance traveled by the vehicle, speed calculation is performed. The accuracy of the algorithm to speed detection is 90.8%.