Multi‐branch network with hierarchical bilinear pooling for person reidentification

Improved Bilinear Pooling with CNNs

Bilinear pooling of Convolutional Neural Network (CNN) features [22, 23], and their compact variants [10], have been shown to be effective at fine-grained recognition, scene categorization, texture recognition, and visual question-answering tasks among others. The resulting representation captures second-order statistics of convolutional features in a translationally invariant manner. In this p...

Hierarchical Cross Network for Person Re-identification

Person re-identification (person re-ID) aims at matching target person(s) grabbed from different and non-overlapping camera views. It plays an important role for public safety and has application in various tasks such as, human retrieval, human tracking, and activity analysis. In this paper, we propose a new network architecture called Hierarchical Cross Network (HCN) to perform person re-ID. I...

Person Reidentification and Recognition in Video

Person recognition has been a challenging research problem for computer vision researchers for many years. A variation of this generic problem is that of identifying the reappearance of the same person in different segments to tag people in a family video. Often we are asked to answer seemingly simple queries such as ‘how many different people are in this video? or ‘find all instances of this p...

A Multi-staged System for Efficient Visual Person Reidentification

An important field in today’s computer vision is person centric video analysis. The basis of this person centric analysis is the detection and tracking of people in video data. In many cases it is not sufficient to track people when they continuously appear in the camera’s field of view, but to also reacquire a track after a person has left a field of view and reenters it. In this paper, we int...

Mixed integer bilinear programming with applications to the pooling problem