Color Distribution Pattern Metric for Person Reidentification

Abstract

Accompanying the growth of surveillance infrastructures, surveillance IP cameras mount up rapidly, crowding Internet of Things (IoT) with countless surveillance frames and increasing the need of person reidentification (Re-ID) in video searching for surveillance and forensic fields. In real scenarios, performance of current proposed Re-ID methods suffers from pose and viewpoint variations due to feature extraction containing background pixels and fixed feature selection strategy for pose and viewpoint variations. To deal with pose and viewpoint variations, we propose the color distribution pattern metric ( ) method, employing color distribution pattern ( ) for feature representation and SVM for classification. Different from other methods, does not extract features over a certain number of dense blocks and is free from varied pedestrian image resolutions and resizing distortion. Moreover, it provides more precise features with less background influences under different body types, severe pose variations, and viewpoint variations. Experimental results show that our method achieves state-of-the-art performance on both 3DPeS dataset and ImageLab Pedestrian Recognition dataset with 68.8% and 79.8% rank 1 accuracy, respectively, under the single-shot experimental setting.

Image monitoring and recognition processing based on neural network

“…Desai et al [5] recognized the human body posture using the neural network of the restricted Boltzmann machine and realized the intelligent opening of the monitoring equipment, which reduced energy consumption and data volume, and verified the feasibility of the scheme by simulation experiments. Training [6] extracted the features of the images collected using the method of color distribution pattern measurement and verifies its recognition accuracy through the simulation experiment of the ImageLab pedestrian recognition data set. This paper briefly introduces the image monitoring and recognition system, as well as the backpropagation (BP) neural network used for identifying the trampling risk area in the monitoring image.…”