In recent years, with increase in concern about public safety and security, human movements or action sequences are highly valued when dealing with suspicious and criminal activities. In order to estimate the position and orientation related to human movements, depth information is needed. This is obtained by fusing data obtained from multiple cameras at different viewpoints. In practice, whenever occlusion occurs in a surveillance environment, there may be a pixel-to-pixel correspondence between two images captured from two cameras and, as a result, depth information may not be accurate. Moreover use of more than one camera exclusively adds burden to the surveillance infrastructure. In this study, we present a mathematical model for acquiring object depth information using single camera by capturing the in focused portion of an object from a single image. When camera is in-focus, with the reference to camera lens center, for a fixed focal length for each aperture setting, the object distance is varied. For each aperture reading, for the corresponding distance, the object distance (or depth) is estimated by relating the three parameters namely lens aperture radius, object distance and object size in image plane. The results show that the distance computed from the relationship approximates actual with a standard error estimate of 2.39 to 2.54, when tested on Nikon and Cannon versions with an accuracy of 98.1% at 95% confidence level.
Infrastructure Supervision is a compelling need for buildings and open areas. It is facilitated through the joint use of stereo vision cameras, techniques and algorithms. This Stereoscopic assessment helps monitoring systems to reconstruct people's visible surface and also provides a robust estimation of the position and posture of the person that allows 3D scene activities and interactions. In practice, in occluded fields, the correspondence between pixels and pixels interferes with the flow of data in surveillance. Structured light ToF imaging and Light Field imaging sensors came into being considering the restriction. These techniques, however failed in addressing the inaccuracies and noise introduced in the phase of profound capture. Based on the Human Anthropometric research, we suggested a technique for estimating the depth of an individual from a single RGB camera. As we deal with moving objects in a scene, also consideration is given to centroid ownership. The system is trained by feeding stature, body width and centroid as inputs to estimate a person's actual height using gradient boosting model. And a person's further anticipated height and actual height are used to predict distance. After taking actual depth (camera to person distance) and real height as ground truth, the suggested model is validated and it is inferred that the camera to person distance anticipated (Preddist) from estimated Real height is 95% correlated with actual Camera to Person distance (or depth) at a confidence level of 99.9% with RMSE of 0.092.
This chapter presents a novel approach for moving object detection and tracking based on the Contour Extraction and Centroid Representation (CECR). Firstly, two consecutive frames are read from the video and they are converted into gray scale. Next, the absolute difference is calculated between them and the result frame is converted into binary by applying gray threshold technique. The binary frame is segmented using contour extraction algorithm. The centroid representation is used for motion tracking. In the second stage of experiment, initially object is detected by using CECR and motion of each track is estimated by kalman filter. Experimental results show that the proposed method can robustly detect and track the moving object.
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