Vehicle re-identification is the task of identifying the same vehicle across multiple cameras.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID.
General Instance Re-identification is a very important task in the computer vision, which can be widely used in many practical applications, such as person/vehicle re-identification, face recognition, wildlife protection, commodity tracing, and snapshop, etc.. To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research.
This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain.
The quadruple directional deep learning networks are with similar overall architecture, including the same basic deep learning architecture but different directional feature pooling layers.
Specifically, in addition to extracting global features, RAM also extracts features from a series of local regions.
In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras.
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).
Top performance in City-Scale Multi-Camera Vehicle Re-Identification demonstrated the advantage of our methods, and we got 5-th place in the vehicle Re-ID track of AIC2020.