2015 International Conference on Circuit, Power and Computing Technologies [ICCPCT] 978-1-4799-7075-9/15/$31.00 ©2015 IEEE Abstract— Vehicle detection is important in traffic monitoring and control. Traditional methods which are based on license plate recognition or vehicle classification which may not be effective for low resolution cameras or when number plate is not available. Also, Vehicle detection in urban scenarios, based on traditional methods like background subtraction fails. To overcome this limitation, this paper present co-training based approach for vehicle detection[1]. Feature selected for detection is haar. Based on haar-training classifier is trained and adaboost is used to get strong classifier. After detection of vehicle, next step is to search for particular vehicles based on its description. Searching of suspicious vehicles is important in criminal investigation. Search framework allows the user to search for vehicles based on attributes such as color, date and time, speed, direction in which vehicle is travelling. Attribute based Vehicle search includes example query "Search for yellow cars moving into horizontal direction from 5.30pm to 8pm".Output of search query is reduced size version of detected vehicles are displayed. Index Terms—— vehicle detection, vehicle tracking, attribute extraction, vehicle search I. INTRODUCTION Automatically Search for suspicious vehicles is important in criminal investigation. Vehicle detection in urban is more challenging due to high volume of data, different weather conditions and different lighting conditions like shadows. In this situation traditional approach like background subtraction fails. This paper presents a method for vehicle detection which works well under these conditions and completes end-to-end system for vehicle retrieval based on semantic attributes. Semantic attribute selected for vehicle search are speed, date and time, colour of detected vehicle and direction in which vehicle is travelling. This paper present training based approach called co-training method for detection. To deal with different types of vehicles like buses, cars, trucks and heavy vehicles deformable aspect ratio sliding window used. II. RELATED WORK Most surveillance system uses background modelling for vehicle detection but it fails in crowded scenes as multiple vehicles are close to each other they are detected as single blob. Appearance based vehicle detection includes work of Fig. 1. Model for Vehicle Detection and Attribute Based Search Fig. 2. Shape free appearance apace b. Changing sliding window[1] viola and Jones [2]. Detection by using edge lets feature [3] and strip feature [4].Support vector machines with histogram of oriented gradients also used for vehicle detection[5][6].Vehicle detection based Statistical learning of object parts proposed by Schneiderman and kanade[7]. These methods showed good performance but it requires large labeling of training samples and works below 15 frames/second . Vehicle detection and Attribute based search of vehicles in video surveillance system Bashirahamad. F. Momin Tabssum. M. Mujawar Department of Computer Science & Engineering Department of Computer Science & Engineering Walchand college of Engineering, Sangli Walchand college of Engineering, Sangli Maharashtra, India Maharashtra, India 1 bfmomin@yahoo.com 2 tmujawar66@gmail.com