FairMOT - 

0) Motivation, Object and Related works:

Motivation:

Objectives:

• They propose two homogeneous branches, detection and ReID branches, to predict pixel-wise objectness scores and ReID features, respectively.

• They adopt DLA-34 which is ResNet-34 with Deep Layer Aggregation (DLA) and convolution layers in all up-sampling modules are replaced by deformable convolution. For the detection branch, there are three parallel heads appended to DLA-34 to estimate heatmaps, object center offsets, and bounding box sizes. The heatmap head estimates the object’s center, the object center offsets head aims to localize object centers more precisely and the bounding box head is responsible for estimating the height and width of objects. In the ReID branch, they applied a convolution layer with 128 kernels on top of backbone features to extract ReID features for each location.

References:

• https://sertiscorp.medium.com/multi-object-tracking-a-review-6aaeea495209

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