Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (1901.03577v1)

Abstract: Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera, foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.

• The paper surveys diverse background subtraction techniques across multiple environments, highlighting challenges in handling dynamic real-world scenes.
• It examines both classic models and advanced deep learning approaches, noting trade-offs between computational efficiency and detection accuracy.
• The study emphasizes practical implementation constraints and proposes hybrid models to bridge the gap between theory and real-world applications.

Background Subtraction in Real Applications: A Survey of Challenges and Current Models

The paper "Background Subtraction in Real Applications: Challenges, Current Models and Future Directions" by Thierry Bouwmans and Belmar Garcia-Garcia provides an extensive survey of the methodologies and challenges associated with the application of background subtraction techniques in various real-world computer vision scenarios. The authors emphasize the necessity for robust and efficient background subtraction methods that can be adapted to a variety of environments, including traffic surveillance, animal behavior studies, maritime monitoring, and more.

Overview of Background Subtraction

Background subtraction is integral to many video-based computer vision applications. It involves separating moving foreground objects from the static background, thereby facilitating tasks such as surveillance, traffic analysis, and wildlife monitoring. This survey categorizes applications based on the environment (e.g., road, maritime, and wildlife) and the nature of foreground objects.

Challenges in Background Subtraction

Different environments introduce unique challenges for background subtraction methods:

1. Environmental Dynamics: Natural environments like forests or underwater habitats possess inherently dynamic backgrounds due to wind, water currents, and varying illumination levels. This complexity demands models capable of handling high variability and periodic background changes.
2. Camera and Sensor Limitations: Surveillance systems often rely on low-quality, stationary cameras. Such limitations necessitate background subtraction methods that minimize computational load and are resilient to noise and artifacts.
3. Foreground Object Characteristics: Foreground objects, such as vehicles, animals, or marine vessels, vary greatly in size, shape, and motion dynamics. Models must accommodate this variability to ensure accurate foreground detection.
4. Practical Implementation Constraints: Real-world applications require methods that are not only effective but also efficient in terms of computation and memory usage, to support real-time processing.

Current Models in Practice

Despite the advances in background subtraction techniques, such as statistical models (e.g., Gaussian Mixture Models), machine learning approaches, and signal processing methods, fundamental applications in real-world scenarios frequently employ simpler, older models like temporal median filters or basic Gaussian models. The gap between advanced methodologies and their practical implementation is often due to the computational and memory demands of newer models, which may not be feasible in real-time applications.

Applications Across Domains

1. Traffic Surveillance: Background subtraction facilitates vehicle tracking and congestion analysis, dealing with challenges like varying lighting conditions and occlusions.
2. Ecological Monitoring: In wildlife and marine studies, it assists in counting and tracking animals or detecting foreign objects, necessitating adaptability to diverse natural environments.
3. Surveillance of Public Spaces: Applications extend to monitoring human activity in public areas, like stores or stadiums, where accurate detection of individuals is critical for security and behavioral analytics.

Future Directions

The paper highlights the potential of robust models based on recent advances in deep learning and subspace learning (e.g., RPCA-based models) to address unsolved challenges in dynamic and complex environments. The authors suggest focusing on hybrid models that leverage the strengths of both classic and modern approaches to create scalable, adaptive solutions suitable for a wide range of applications.

Conclusion

This survey not only compiles various background subtraction techniques but also underscores the need to bridge the gap between theoretical advancements and their real-world applications. By addressing the specific challenges of distinct environments and integrating scalable approaches, future research can enhance the applicability and effectiveness of background subtraction techniques across diverse domains. The ongoing development of efficient and versatile background models holds the key to broader adoption and integration into critical surveillance and monitoring systems worldwide.