An Automatic Traffic Surveillance System for Vehicle Tracking and Classification

Abstract

This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods which can classify vehicles to only cars and non-cars, the proposed method has a good capability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by taking advantages of a line-based shadow algorithm which uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of different lane dividing lines. Therefore, an automatic scheme to detect lane dividing lines is also proposed. The found lane dividing lines also can provide important information for feature normalization, which can make the vehicle size more invariant and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make the best decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only vehicle size and single frame for classification.

Internet of Things: Architectures, Protocols, and Applications

“…Custom applications can analyze traffic patterns so that future traffic conditions can be estimated. Yu et al [85] implement a vehicle tracking system for traffic surveillance using video sequences captured on the roads.…”

SMART CROP FIELD IRRIGATION IN IoT ARCHITECTURE USING SENSORS

“…. [6] implemented a vehicle tracking system for managing traffic surveillance using video sequencing captured on the roads. Smart transport does not responsible for managing traffic conditions but also gets rid of the safety of people travelling in vehicles, which is mainly in the hands of drivers.…”

Robust Real‐Time Traffic Surveillance with Deep Learning

“…Usage of video cameras in traffic surveillance [ 7 – 9 ] typically was limited to passive monitoring tasks or very basic automated processing. The advances in image processing algorithms in the last decade specially in the deep neural networks area have opened the door to more sophisticated systems based on computer vision.…”