Tracking-Learning-Detection

Citations Over TimeTop 1% of 2011 papers

Abstract

This paper investigates long-term tracking of unknown objects in a video stream. The object is defined by its location and extent in a single frame. In every frame that follows, the task is to determine the object's location and extent or indicate that the object is not present. We propose a novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning, and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates the detector's errors and updates it to avoid these errors in the future. We study how to identify the detector's errors and learn from them. We develop a novel learning method (P-N learning) which estimates the errors by a pair of "experts": (1) P-expert estimates missed detections, and (2) N-expert estimates false alarms. The learning process is modeled as a discrete dynamical system and the conditions under which the learning guarantees improvement are found. We describe our real-time implementation of the TLD framework and the P-N learning. We carry out an extensive quantitative evaluation which shows a significant improvement over state-of-the-art approaches.

Related Papers

• → A Performance of Low-Cost NVIDIA Jetson Nano Embedded System in the Real-Time Siamese Single Object Tracking: A Comparison Study(2022)7 cited
• Recovery method based particle filter for object tracking in complex environment(2012)
• → The Augmented Laboratory – 3D, Multiple Object Tracking(2019)1 cited
• → Some Cases of Object Tracking Parametrs Optimization in Video Stream(2021)
• → Online Learning Sample Filtering for Object Tracking(2023)