Object Tracking

Object tracking is a fundamental task in computer vision that involves locating and following one or more moving objects over time through a sequence of video frames. Unlike object detection, which identifies objects in a single image, tracking aims to associate these detections across frames to generate a consistent trajectory or path for each object, even when faced with challenges like occlusion or changes in appearance. By analyzing the temporal and spatial information, tracking algorithms are essential for a wide range of applications, including autonomous vehicle navigation, video surveillance, robotics, and sports analytics.

  1. Introduction to Object Tracking
    1. Defining Object Tracking
      1. Description of the Tracking Task
        1. Types of Objects Tracked
          1. Temporal Aspect of Tracking
          2. Core Problem Statement
            1. Input and Output of Tracking Systems
              1. Challenges in Continuous Localization
              2. Distinction from Object Detection
                1. Frame-level Detection vs Temporal Association
                  1. Complementary Roles in Vision Pipelines
                  2. Key Terminology
                    1. Target
                      1. Trajectory
                        1. State
                          1. Observation
                            1. Frame
                            2. Historical Context and Evolution
                              1. Early Tracking Methods
                                1. Milestones in Algorithm Development
                                  1. Impact of Deep Learning

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                                2. Fundamental Concepts and Components