2 research outputs found
Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking
Multi-target multi-camera tracking (MTMCT) systems track targets across
cameras. Due to the continuity of target trajectories, tracking systems usually
restrict their data association within a local neighborhood. In single camera
tracking, local neighborhood refers to consecutive frames; in multi-camera
tracking, it refers to neighboring cameras that the target may appear
successively. For similarity estimation, tracking systems often adopt
appearance features learned from the re-identification (re-ID) perspective.
Different from tracking, re-ID usually does not have access to the trajectory
cues that can limit the search space to a local neighborhood. Due to its global
matching property, the re-ID perspective requires to learn global appearance
features. We argue that the mismatch between the local matching procedure in
tracking and the global nature of re-ID appearance features may compromise
MTMCT performance.
To fit the local matching procedure in MTMCT, in this work, we introduce
locality aware appearance metric (LAAM). Specifically, we design an
intra-camera metric for single camera tracking, and an inter-camera metric for
multi-camera tracking. Both metrics are trained with data pairs sampled from
their corresponding local neighborhoods, as opposed to global sampling in the
re-ID perspective. We show that the locally learned metrics can be successfully
applied on top of several globally learned re-ID features. With the proposed
method, we report new state-of-the-art performance on the DukeMTMC dataset, and
a substantial improvement on the CityFlow dataset
Deep Learning for Person Re-identification: A Survey and Outlook
Person re-identification (Re-ID) aims at retrieving a person of interest
across multiple non-overlapping cameras. With the advancement of deep neural
networks and increasing demand of intelligent video surveillance, it has gained
significantly increased interest in the computer vision community. By
dissecting the involved components in developing a person Re-ID system, we
categorize it into the closed-world and open-world settings. The widely studied
closed-world setting is usually applied under various research-oriented
assumptions, and has achieved inspiring success using deep learning techniques
on a number of datasets. We first conduct a comprehensive overview with
in-depth analysis for closed-world person Re-ID from three different
perspectives, including deep feature representation learning, deep metric
learning and ranking optimization. With the performance saturation under
closed-world setting, the research focus for person Re-ID has recently shifted
to the open-world setting, facing more challenging issues. This setting is
closer to practical applications under specific scenarios. We summarize the
open-world Re-ID in terms of five different aspects. By analyzing the
advantages of existing methods, we design a powerful AGW baseline, achieving
state-of-the-art or at least comparable performance on twelve datasets for FOUR
different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP)
for person Re-ID, indicating the cost for finding all the correct matches,
which provides an additional criteria to evaluate the Re-ID system for real
applications. Finally, some important yet under-investigated open issues are
discussed.Comment: 20 pages, 8 figures. Accepted by IEEE TPAM