Abstract: Congestion in traffic is a serious problem nowadays. From city roads to highways, a lot of traffic problems occur everywhere in today’s world. Bad traffic management leads to wastage of lot of man hours. These frequent traffic problems like traffic jams have lead to the rise of the need for an efficient traffic management method. Current traffic control techniques involving magnetic loop detectors buried inside the road, infra-red and radar sensors  on the side provide limited traffic information and require separate systems for traffic counting and for traffic surveillance. In contrast, video based systems offer many advantages compared t existing technologies . There can be many reasons for bursty traffic like insufficient capacity, unrestrained demands, large red light delays etc. This paper summarizes a review on various methods for developing an intelligent traffic detection algorithm based on different research papers referred. It also shows a review on different methods under image processing for developing an intelligent traffic system. Various methods on this topic have been explored on this topic like, intelligent traffic controller using real time image processing-, using DSP and NIOS II , using embedded system and using wireless sensor network . Various authors use different techniques like detecting the subsequent numbers of vehicles detected from the video captured using the cameras installed at the lanes or using live feed from cameras at traffic junction for real time traffic density calculation using image and video processing or making use of wireless sensors to sense presence of traffic on roads. Comparison and survey of all these methods is shown in this paper which concluded that use of image processing makes analysis of traffic comparatively efficient. Reasons proving the same are discussed in paper below.
Keywords: Traffic density analysis, image and video processing, reduced traffic congestion, traffic management, digital image processing.