On the Real Time Object Detection and Tracking

Object detection and tracking is widely used for detecting motions of objects present in images and video.Since last so many decades, numerous real time object detection and tracking methods have been proposed byresearchers. The proposed methods for objects to be tracked till date require some preceding informationassociated with moving objects. In real time object detection and tracking approach segmentation is the initialtask followed by background modeling for the extraction of predefined information including shape of the objects,position in the starting frame, texture, geometry and so on for further processing of the cluster pixels and videosequence of these objects. The object detection and tracking can be applied in the fields like computerized videosurveillance, traffic monitoring, robotic vision, gesture identification, human-computer interaction, militarysurveillance system, vehicle navigation, medical imaging, biomedical image analysis and many more. In thispaper we focus detailed technical review of different methods proposed for detection and tracking of objects. Thecomparison of various techniques of detection and tracking is the purpose of this work

Online Structured Sparsity-based Moving Object Detection from Satellite Videos

Inspired by the recent developments in computer vision, low-rank and structured sparse matrix decomposition can be potentially be used for extract moving objects in satellite videos. This set of approaches seeks for rank minimization on the background that typically requires batch-based optimization over a sequence of frames, which causes delays in processing and limits their applications. To remedy this delay, we propose an Online Low-rank and Structured Sparse Decomposition (O-LSD). O-LSD reformulates the batch-based low-rank matrix decomposition with the structured sparse penalty to its equivalent frame-wise separable counterpart, which then defines a stochastic optimization problem for online subspace basis estimation. In order to promote online processing, O-LSD conducts the foreground and background separation and the subspace basis update alternatingly for every frame in a video. We also show the convergence of O-LSD theoretically. Experimental results on two satellite videos demonstrate the performance of O-LSD in term of accuracy and time consumption is comparable with the batch-based approaches with significantly reduced delay in processing