Video Tracking 2021
Classic object tracking
classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection.
Kalman filtering, sparse and dense optical flow,
Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter
SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results.
The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis.
Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences.
Multi-object tracking datasets
large-scale benchmark Multi-Class Multi-object tracking datasets
VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking.
the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue.
Temporal coherence. A feasible way to exploit temporal coherence is using object trackers
Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance.
List of Datasets
PyTorch 0.4.1: link
TensorFlow 1.13.1: link
PyTorch 1.3.1: link
PyTorch ≥ 1.2.0: link
Self collected datasets
 Vision Meets Drones: Past, Present and Future