Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment.
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.
SOTA for Visual Object Tracking on VOT2017/18
Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms.
Visual object tracking has been a fundamental topic in recent years and many deep learning based trackers have achieved state-of-the-art performance on multiple benchmarks.
#8 best model for Visual Object Tracking on VOT2017/18
During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors.
#4 best model for Visual Object Tracking on VOT2017/18
We also carry out a series of analytical experiments to select a compact while highly representative testing subset -- it embodies 84 object classes and 32 motion patterns with only 180 video segments, allowing for efficient evaluation.
Online multi-object tracking is a fundamental problem in time-critical video analysis applications.
In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.
In this project, we implement a multiple object tracker, following the tracking-by-detection paradigm, as an extension of an existing method.