Efficient Motion Estimation and Detection, Background Subtraction, Shadow Removal and Occlusion Detection. Prof. Pravin R. Lakhe Prof. Dr. V. U. Kale Deapartment of EXTC Engg. Department of EXTC Engg. Prof. Ram Meghe Institute of tech. Prof. Ram Meghe Institute of tech. and research, Badnera, Amravati and research, Badnera, Amravati Abstract The main objective of this project is to develop multiple human object tracking approach based on motion estimation and detection, background subtraction, shadow removal and occlusion detection. A reference frame is initially used and considered as background information. While a new object enters into the frame, the foreground information and background information are identified using the reference frame as background model. Most of the times, the shadow of the background information is merged with the foreground object and makes the tracking process a complex one. The algorithm involves modeling of the desired background as a reference model for later used in background subtraction to produce foreground pixel which is deviation of the current frame from the reference frame. In the approach, morphological operations will be used for identifying and removed the shadow. The occlusion is one of the most common events in object tracking and object centroid of each object is used for detecting the occlusion and identifying each object separately. Video sequences will be captured and will be detected with the proposed algorithm. Keywords: Background modeling and subtraction, human motion detection, object tracking, shadow removal, occlusion. 1. Introduction Object tracking can be defined as the process of segmenting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful information. Object tracking in video processing follows the segmentation step and is more or less equivalent to the „recognition‟ step in the image processing. Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications, including traffic monitoring, automated remote video surveillance, and people tracking. The capability of extracting moving objects from a video sequence is a fundamental and crucial problem of many vision systems that include video surveillance [1,2],traffic monitoring [3], human detection and tracking for video teleconferencing or human-machine interface [4, 5, 6], video editing, among other applications. In applications using fixed cameras with respect to the static background (e.g. stationary surveillance cameras), a very common approach is to use background subtraction to obtain an initial estimate of moving objects. Basically, background subtraction consists of comparing each new frame with a representation of the scene background: significative differences usually correspond to foreground objects. Ideally, background subtraction should detect real moving objects with high accuracy, limiting false negatives (objects pixels that are not detected) as much as possible; at the same time, it should extract pixels of moving objects with the maximum responsiveness possible, avoiding detection of transient spurious objects, such as cast shadows, static objects, or noise. In this paper, we present a shadow removal technique which effectively eliminates a human shadow cast from an unknown direction of light source. A multi-cue shadow descriptor is proposed to characterize the distinctive properties of shadows. We employ a 3-stage process to detect then remove shadows. Our algorithm improves the shadow detection accuracy by imposing the spatial constraint between the foreground subregions of human and shadow. The existence of human shadows is a general problem in tracking and recognizing human activities. Shadows not only distort the color properties of the area being shaded but also complicate the edge structure of the figure as a International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 9, November- 2012 ISSN: 2278-0181 1 www.ijert.org