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12-05-2017, 10:23 AM

[b]matlab code for tracking moving object using bounding box techniques[/b]

jaseela123d · 13-05-2017, 03:48 PM

Detection and tracking of mobile objects is often the first step in applications such as video surveillance. Tracking objects is a very important task within the area of computer vision. It is used to monitor the spatial and temporal changes of an object during a video sequence, including its characteristics such as presence, position, size, shape, etc. This is done by solving the problem of temporal correspondence, the question of matching the target region in successive frames of a sequence of images taken at very spaced intervals of time. There are three key steps in video analysis: detecting interesting moving objects, tracking frame-by-frame objects, and analyzing object tracks to identify their behavior. Figure 1 shows the basic steps of the video-based object tracking system. Object tracking can be more complex due to loss of information caused by the projection of the 3D world into a 2D image, noise in images, movement of complex objects, forms of complex objects, non-rigid or articulated nature of objects , Scene lighting changes, occlusion of complete objects and processing requirements in real time. The proliferation of high-powered computers, the accessibility of high-quality and affordable video cameras, and the growing need for automated video analysis have generated great interest in object tracking methods. A tracker assigns trusted tags to objects tracked in different frames of a video.

Object detection and tracking has a wide variety of applications in computer vision such as video surveillance, video compression, vision-based control, human-computer interfaces, robotic medical imaging, and augmented reality. It also provides input to top-level vision tasks such as 3D reconstruction and 3D rendering. It plays an important role in the video database, such as content-based indexing and retrieval. Fig: Typical object tracking system II. BACKGROUND The object can be represented as a single point or a set of points. It can represent as primitive geometric forms as rectangle, orellipse circle. It can be object outline, object silhouette, articulated shape models, skeletal modeletc. Shape representation is combined with appearance representations for the purpose of tracking. Some of the aspect representations used are object appearance probability densities, templates, active appearance model, and multi-view appearance model. The selection of functions is closely related to the representation of the object. Some of the commonly used features are edges, color, texture, gradient, optical flow, etc. Each tracking method requires an object detection mechanism in each frame or when the object appears in the video. Some commonly used object detection methods are: background subtraction, spot detectors, segmentation, and learning supervision.

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