With the invention of autonomous vehicles, it make easier to know about the future directions of the vehicles. The main purposes of vehicle detection and tracking are to identify the on-road traffic conditions, hazards or hurts as well as to communicate with other on-road vehicles/objects. To meet the above requirements, the following methodologies are useful which are like Fuzzy based, Radar and V2V fusion, Background subtraction, Active contour, Single learning based method and etc., Those methodologies are implementing either independently or collaboratively may lead to focus towards not only on the vehicles and other objects of our interest. In order to track the vehicles, first to detect the various objects and from them only it is possible to identify the vehicle objects. After the identification of vehicular objects, we can segregate them according to their sub-category. In this paper, various object/vehicle detection techniques have been discussed. The process of segmentation and separation of vehicle objects and its types can be possible by implementing a certain fuzzy rules. Detection of an object using various fusion techniques makes more effective in terms of obtaining the information regarding that particular vehicle directly or via nearby vehicles. While capturing the object, there is a possibility of false detection. This can be avoided by clearly eliminating the shadows, illumination and etc., from capturing object image. This kind of elimination process is termed as background subtraction. Active contour is one another detection method which gives us succession of images from which the internal and external borders of several objects can be identified. By a single extraction various objects are identified and then given to different detectors according to their sub-category. All these kind of techniques are discussed in this paper.
Comparison of Vehicle Detection Techniques applied to IP Camera Video Feeds for use in Intelligent Transport Systems
