Person Re-identification with feature Aggregation
DOI:
https://doi.org/10.26438/ijcse/v7i6.466469Keywords:
Person Re-identification, Metric Learning, Feature Aggregation, HOG descriptorAbstract
Person Re-identification (re-ID) is a critical problem in video analytics applications such as security and surveillance. Although many approaches have been proposed, it remains a challenging problem since persons appearance usually undergoes dramatic changes across camera views due to changes in view angle, body pose and background clutter. Person re-id aims to retrieve a person of interest across spatially disjoint cameras. The system focuses on tackling the person re-ID problem with the proposed metric learning scheme. There is a discriminant metric learning strategy for this testing issue. Most existing metric learning algorithms, it takes both original data and auxiliary data during training which is motivated by the new machine learning paradigm - Learning Using Privileged Information. This system is based on features aggregation. Image dataset is load and the basic operation is performing that is to convert those load images into gray scale. And also create the HOG (Histogram of oriented gradient) descriptor, in this features extraction task completed based on EHD (Edge of histogram descriptor), CLD (Color Layout descriptor), and SCD (Scale Color descriptor). The system aggregates all Features and Generate Train metric. After that an unknown image is load which is comes through gray scale process and HOG descriptor. Classify that images and identify the correct image. Such system is used in many sectors for security purpose.
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