Omnidirectional Feature Learning for Person Re-Identification
نویسندگان
چکیده
منابع مشابه
Hierarchical Invariant Feature Learning with Marginalization for Person Re-Identification
This paper addresses the problem of matching pedestrians across multiple camera views, known as person re-identification. Variations in lighting conditions, environment and pose changes across camera views make re-identification a challenging problem. Previous methods address these challenges by designing specific features or by learning a distance function. We propose a hierarchical feature le...
متن کاملPerson Re-identification via Recurrent Feature Aggregation
We address the person re-identification problem by effectively exploiting a globally discriminative feature representation from a sequence of tracked human regions/patches. This is in contrast to previous person re-id works, which rely on either single frame based person to person patch matching, or graph based sequence to sequence matching. We show that a progressive/sequential fusion framewor...
متن کاملScience Deep learning for person re - identification
Person re-identification is the task of ranking a gallery of automatically detected images of persons using a set of query images. This is challenging due to the different poses, viewpoints, occlusions, camera configurations, image distortions, lighting conditions, image resolutions and imperfect detections, which all affects a person re-identification system’s performance. Recently deeply lear...
متن کاملLearning Appearance Transfer for Person Re-identification
In this chapter we review methods that model the transfer a person’s appearance undergoes when passing between two cameras with non-overlapping fields of view. Whereas many recent studies deal with re-identifying a person at any new location and search for universal signatures and metrics, here we focus on solutions for the natural setup of surveillance systems in which the cameras are specific...
متن کاملMahalanobis Distance Learning for Person Re-identification
Recently, Mahalanobis metric learning has gained a considerable interest for single-shot person re-identification. The main idea is to build on an existing image representation and to learn a metric that reflects the visual camera-to-camera transitions, allowing for a more powerful classification. The goal of this chapter is twofold. We first review the main ideas of Mahalanobis metric learning...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2901764