Recognition of 3D compressed images and its traffic monitoring applications

In a digital image network for traffic monitoring a large number of cameras are connected to control centers through a hierarchical network. Compressed image data and recognition results are transmitted over the network. With conventional approaches, each control center receives compressed image data along with preliminary recognition results from low level control centers or surveillance cameras. Each center needs to decompress image data for further recognition processing, and if necessary the center sends the compressed image data and recognition results to the upper-level control center. In order to increase the cost-efficiency of the digital image network, we propose eliminating the decompression required at each center by developing a recognition method which works in the compressed domain. The main stream of conventional image compression methods such as discrete cosine transform is based on spatial frequency which makes it difficult to carry out recognition processes in the compressed domain. In contrast, we will compress the image data by using attributes which are relevant both for compression and recognition. Examples of the common attributes are binary edge locations and the color information surrounding the edge. This and other information is retained in the compression domain to enable recognition without decompression.