Visual Tracking
170 papers with code • 9 benchmarks • 27 datasets
Visual Tracking is an essential and actively researched problem in the field of computer vision with various real-world applications such as robotic services, smart surveillance systems, autonomous driving, and human-computer interaction. It refers to the automatic estimation of the trajectory of an arbitrary target object, usually specified by a bounding box in the first frame, as it moves around in subsequent video frames.
Source: Learning Reinforced Attentional Representation for End-to-End Visual Tracking
Libraries
Use these libraries to find Visual Tracking models and implementationsMost implemented papers
Tracking Holistic Object Representations
The framework leverages the idea of obtaining additional object templates during the tracking process.
GradNet: Gradient-Guided Network for Visual Object Tracking
In this work, we propose a novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations.
SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking
The proposed framework SiamCAR consists of two simple subnetworks: one Siamese subnetwork for feature extraction and one classification-regression subnetwork for bounding box prediction.
Siamese Box Adaptive Network for Visual Tracking
Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-defined anchor boxes to accurately estimate the scale and aspect ratio of a target.
Probabilistic Regression for Visual Tracking
In this work, we therefore propose a probabilistic regression formulation and apply it to tracking.
High-Performance Long-Term Tracking with Meta-Updater
Most top-ranked long-term trackers adopt the offline-trained Siamese architectures, thus, they cannot benefit from great progress of short-term trackers with online update.
Fully Convolutional Online Tracking
To tackle this issue, we present the fully convolutional online tracking framework, coined as FCOT, and focus on enabling online learning for both classification and regression branches by using a target filter based tracking paradigm.
Multi-modal Visual Tracking: Review and Experimental Comparison
Visual object tracking, as a fundamental task in computer vision, has drawn much attention in recent years.
Predictive Visual Tracking: A New Benchmark and Baseline Approach
As a crucial robotic perception capability, visual tracking has been intensively studied recently.
Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark
We believe this benchmark will greatly boost related researches on natural language guided tracking.