Artificial Intelligence

   

Implementation, Comparison and Literature Review of Spatio-Temporal and Compressed Domains Object Detection

Authors: Gokul Krishna Srinivasan

Object detection in a moving video stream is playing a prominent role in every branch of science and research[1]. Objection detection or tracking is done by two different methods, namely, spatio-temporal domain and compressed domain. This project will deal with both the domains in order to bring out the advantages and disadvantages of each and every method in terms of complexity in computations, efficiency etc. Along with that, a detailed literature survey will also be done on the same topic. Most image and video data are stored or transmitted after compression for efficiency. Processes like pattern detection and localization typically include the extra expense of decompressing the data since most image and video processing techniques require access to the original pixel values in the spatial domain. This project performs a statistical analysis of present object detection schemes in both spatio-temporal and compressed domains. The results of multiple object detection in spatio-temporal domain are compared to those of compressed domain object detection and various evaluation results are analyzed. The comparisons will be made in the context of complexity of algorithm, efficiency and application.

Comments: 12 Pages.

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[v1] 2019-01-15 13:55:11

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