Automatic Handgun Detection Alarm in Videos Using Deep Learning (1702.05147v1)

Abstract: Current surveillance and control systems still require human supervision and intervention. This work presents a novel automatic handgun detection system in videos appropriate for both, surveillance and control purposes. We reformulate this detection problem into the problem of minimizing false positives and solve it by building the key training data-set guided by the results of a deep Convolutional Neural Networks (CNN) classifier, then assessing the best classification model under two approaches, the sliding window approach and region proposal approach. The most promising results are obtained by Faster R-CNN based model trained on our new database. The best detector show a high potential even in low quality youtube videos and provides satisfactory results as automatic alarm system. Among 30 scenes, it successfully activates the alarm after five successive true positives in less than 0.2 seconds, in 27 scenes. We also define a new metric, Alarm Activation per Interval (AApI), to assess the performance of a detection model as an automatic detection system in videos.

• The paper introduces a CNN-based system that uses both sliding window and region proposal approaches to balance detection accuracy and execution speed.
• It demonstrates a 100% recall for pistols with Faster R-CNN, validating the use of deep learning for real-time alarm activation in surveillance applications.
• The study emphasizes dataset refinement and transfer learning with VGG-16, leading to enhanced model precision and reduced false positives in challenging video scenarios.

Automatic Handgun Detection Alarm in Videos Using Deep Learning: An Overview

The paper, "Automatic Handgun Detection Alarm in Videos Using Deep Learning," offers a comprehensive exploration into leveraging deep learning techniques, specifically Convolutional Neural Networks (CNNs), for real-time handgun detection in video surveillance systems. The work is guided by the imperative need for enhanced detection mechanisms given the prevalence of crimes involving firearms.

Problem Formulation and Approach

The authors address the problem of handgun detection by converting it into a false positive minimization issue. Two methodological approaches—the sliding window and region proposal—are employed to evaluate the efficacy of CNN-based classifiers. These approaches are distinct in their handling of candidate detection regions within image frames.

1. Sliding Window Approach: This exhaustive method involves scanning a fixed-size window across various scales and positions within an image. While providing satisfactory detection precision, this approach is computationally expensive and less suited for real-time applications due to its marginal recall and longer processing times.
2. Region Proposal Approach: By refining candidate regions using selective methods, Faster R-CNNs demonstrate superior performance in handgun detection, ensuring high recall (100% for pistols) without compromising on detection speed. This approach effectively balances accuracy and execution time, providing a feasible solution for real-time surveillance applications.

Dataset Construction and Model Training

Critical to the success of this research is the construction of a specialized dataset that addresses the intricate features distinguishing handguns from other objects. The dataset's effectiveness is pivotal, as demonstrated through the superior results obtained with a finely-tuned Faster R-CNN model trained on extensive handgun imagery reflecting diverse contexts.

• Database Optimization: The paper emphasizes dataset refinement, including enlarging class diversity to effectively combat false positives. The authors evaluated various configurations, leading to improved precision but necessitating optimal selection for practical deployment.
• Transfer Learning Utilization: The VGG-16 architecture is leveraged for fine-tuning, supporting the idea of transfer learning as a viable strategy for overcoming constraints posed by limited dataset size. This approach ensures that low-level features are maintained while optimizing high-level layers to recognize handgun-specific characteristics.

Experimental Results and Metrics

Extensive testing using low-quality YouTube video footage illustrates the practical capability of the proposed system. Success in activating alarms in 27 out of 30 evaluated scenes confirms the system's applicability in realistic surveillance environments.

• Performance Metrics: The paper introduces Alarm Activation Time per Interval (AATpI), a metric gauging the rapidity of alarm activation upon successive true positive detection across frames. This metric underscores the system's utility in environments requiring immediate recognition and reaction.

Implications and Future Work

From a theoretical standpoint, this research enriches the deep learning domain by offering insights into effective application-driven CNN model adaptations. Practically, it suggests a pathway for developing automated security systems capable of discerning threats in real time, which is crucial for law enforcement and public safety domains.

Looking forward, the authors indicate intentions to diminish false positives further and enhance detection amidst adverse visual conditions by integrated preprocessing. Additionally, exploring alternative CNN architectures is proposed to potentially bolster detection robustness and computational efficiency.

In conclusion, this paper lays foundational groundwork for the deployment of real-time deep learning-based handgun detection systems, demonstrating measurable success in early detection and intervention strategies within surveillance contexts.