Person Retrieval and Re-Identification

Description: Person Re-ID becomes a challenging task when there are lot of disturbances in the image such as occlusion, person-box aspect ratios, crop scales, and training the re-id models with present approaches of supervised classification and triplet matching when the number of classes are very high. Training the models on the public datasets like Duke-MTMC and Market1501 does not perform well on the issues mentioned as they are prominent in the real-world scenarios. I, therefore to tackle these problems, designed a Re-ID model which can take into consideration the box scale and alignment of a person crop for the best retrieval accuracy. I tested it on many datasets and I am mentioning the results on Market1501 using the proposed algorithm. Also, there was a significant gain observed when it was tested on the private company’s datasets.

Model	Data	Rank@1	Rank@5	Rank@10	mAP
Baseline	Market1501	94.63	98.25	99.05	85.24
Baeline + Alignment + MultiScale	Market1501	96.59	98.84	99.44	91.24