From surveillance systems that safeguard our cities to autonomous vehicles navigating our roads, object tracking has emerged as a fundamental technology in computer vision.

This article delves into object tracking, exploring its underlying principles, diverse methodologies, and real-world applications.

What is Object Tracking?

Object tracking is an essential application of deep learning extensively used in computer vision. It refers to automatically recognizing and tracing objects across the frames in a dynamic environment by analyzing the trajectories once the initial position is known.

Object tracking implicitly uses techniques to identify and classify the objects in a frame and associate a unique identification to each. Typically, detected objects are shown using visual indicators, such as a bounding box or feature maps in DL models’ case, to represent their location. The ultimate goal of object tracking is to achieve a highly accurate and reliable interpretation of objects as they navigate within the video frames.

Different Types of Object Tracking

Object tracking has different modes depending on the scope and nature of the tracking process:

Video Tracking

Video tracking is a type of object tracking to identify and track moving objects in a real-time change feed or footage. It considers the temporal continuity between the frames and utilizes the information from past frames to assist in the tracking process. This finds applications in security and surveillance, autonomous self-driving vehicles, traffic monitoring, etc.

Image Tracking

This involves detecting a two-dimensional image and monitoring its set of trajectories frame by frame. In this case, the tracking algorithm operates on individual images independently without considering any temporal information. It is employed for datasets containing images with distinct differences and contrasting characteristics than that of the setting, lack of symmetry, limited patterns, and multiple noticeable distinctions between the target image and other images within the dataset.

In summary, image tracking deals with object localization within individual images, while video tracking involves tracking objects across multiple frames to maintain their continuity and trajectory.

Levels of Object Tracking

There are three levels or aspects that one needs to consider in the context of object tracking.

Single Object Tracking

This level of object tracking is considered the easiest of all, as the focus lies on tracking a single object of interest throughout all video frames. The goal is to observe and derive a feature set about the object’s position, size, and other attributes as it is traced over time. Single object tracking techniques are often used in scenarios where complete analysis of the object is required and hence involve advanced techniques like motion cues, appearance models, or feature matching to maintain the continuity of the region of interest.

Multiple Object Tracking

Multiple object tracking is single object tracking on a broader scale. It deals with monitoring and maintaining the trajectories of various objects simultaneously in a video sequence. The potential limitation that hampers this level of object tracking is occlusions caused by interactions among the objects in the dynamic set of environments. Multiple object tracking methods involve object detection, data association, and tracking-by-detection techniques to handle these complexities and accurately track various objects over time. This is most used in scenarios where the multi-dimensional tracking of the environment is needed, like in surveillance systems, self-driving cars, etc.

Future Scope of Levels of Object Tracking

One step ahead of multi-object tracking is extracting high-level features, which can utilize the information of position and trajectories of multiple objects in the view to predict future actions. This level of tracking would be an amalgamation of machine learning and computer vision techniques to extract meaningful insights from the tracked object’s motion patterns [Multiple Object Tracking in Deep Learning Approaches: A Survey (mdpi.com)].

Each level of object tracking has its own set of challenges and complexities, hence employing a different tracking process. Single object tracking lays the foundation, multiple object tracking extends it to handle multiple entities, and high-level tracking adds a semantic and intent understanding of object behavior and scene dynamics. The choice of the tracking level entirely depends on the use case.

For more information, you can go through Object Tracking: A survey by Yilmaz et al.

Applications of Object Tracking

With the increase in automation and industrialization, object tracking algorithms are extensively used in scenarios where constant video monitoring is required with high accuracy and reliability using minimum human resources.

1. Surveillance and Security: Object tracking is widely used in surveillance systems for monitoring and tracking individuals or objects of interest within a scene. It helps in identifying suspicious activities, tracking intruders, detecting unauthorized objects or theft protection in banks, shopping complexes, military units, government offices, etc.

2. Autonomous Vehicles: Autonomous vehicles cannot function without the knowledge of object tracking. They perceive and track other vehicles, pedestrians, and objects in their surroundings and react accordingly in collision avoidance, path planning, and maintaining situational awareness.

3. Augmented Reality (AR): Object tracking identifies 2D objects and overlays virtual objects onto the natural world in AR applications. By tracking real-world objects, virtual content can be precisely aligned and traced along with the things within the frames. It is used in E-Commerce to help buyers visualize the overall look of the object in the real world.

4. Robotics: Robotic applications use object detection techniques to track objects to perform tasks, follow targets, or recognize and interact with humans.

5. Video Analysis and Understanding: Object tracking is employed in human-computer interaction scenarios to track hand gestures, behavior analysis using facial expressions or body movements, and anomaly detection. It helps track objects of interest over time, understand their interactions, and extract meaningful insights from video data.

6. Sports Analytics: Object tracking is extensively used in sports analytics to track players, balls, and other objects during games. It provides valuable data for performance analysis, generating visualizations, and making crucial decisions for sports broadcasts.

7. Medical Imaging: Object tracking is applied in medical imaging for tracking organs, tumors, or specific anatomical features in medical scans or videos. It assists in surgical guidance, radiation therapy, and monitoring disease progression over time.

Limitations of Object Tracking

Object tracking is not a simple thing to crack, as many dependencies decide whether tracking is accurate or not — the object, the surrounding objects, and the background. This section lays out the main challenges.

Challenges in the quality of the object

The tracked object can be of any size or aspect ratio. The level of granularity and distinctive boundaries help better extract the feature map when training or identifying the object. Hence, object shape, size, color, and illuminance significantly impact the object-tracking algorithm.

Challenges in the background

Background blurriness and distractions with densely populated backgrounds make extracting features maps difficult. In such situations, the feature set is very sparse, and redundant features introduce noise that can obscure the main features. Datasets with footage of better light conditions and color contrast tend to detect objects more accurately.

Challenges in the surroundings of the object

In densely populated surroundings, object tracking becomes difficult due to occlusion. Multiple objects may surround the object nearby, which gives a visual representation of a single overlapped object. In such scenarios, it is not possible to identify which part of the object holds more critical information.

Speed of tracking

Another challenge apart from the data is the speed with which the training and tracking of objects happen. The base of object tracking is a multi-task algorithm that starts with setting an initial reference from which the object is identified, localized view, and tracing along all the video frames.

Summary

In our exploration of object tracking within the realm of computer vision, we’ve delved into various facets of this dynamic field, examining its types, levels, applications, and the inherent challenges it presents.

1. Types of Object Tracking: We began by understanding the different types of object tracking, from image tracking to more complex video tracking. These distinctions are crucial in determining the appropriate tracking approach for specific applications.
2. Levels of Object Tracking: We then discussed the levels of object tracking, categorizing them into single-object and multi-object. We also looked at the future scope of object tracking .
3. Applications, Limitations and Challenges: Our journey took us through the diverse applications of object tracking, emphasizing its importance in surveillance, robotics, autonomous vehicles, and augmented reality. We also acknowledged the limitations and challenges faced by object tracking systems, including occlusion, scale variation, and the need for robustness in complex environments. Understanding these limitations is essential for driving innovations in the field.

Object tracking in computer vision is a dynamic, multidimensional discipline with profound implications across industries. It is the backbone of technologies that enable us to monitor, interact with, and understand the world around us. As we conclude our exploration, we recognize the significance of striking a balance between the types and levels of object tracking to address diverse real-world challenges effectively.