Detection and Localization of Firearm Carriers in Complex Scenes for Improved Safety Measures

• URL: http://arxiv.org/abs/2309.09236v1
• Date: Sun, 17 Sep 2023 10:50:46 GMT
• Title: Detection and Localization of Firearm Carriers in Complex Scenes for Improved Safety Measures
• Authors: Arif Mahmood, Abdul Basit, M. Akhtar Munir, Mohsen Ali
• Abstract summary: We propose a novel approach that leverages human-firearm interaction information, which provides valuable clues for localizing firearm carriers. Our approach incorporates an attention mechanism that effectively distinguishes humans and firearms from the background by focusing on relevant areas. To handle inputs of varying sizes, we pass paired human-firearm instances with attention masks as channels through a deep network for feature computation.
• Score: 17.03422574286601
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Detecting firearms and accurately localizing individuals carrying them in images or videos is of paramount importance in security, surveillance, and content customization. However, this task presents significant challenges in complex environments due to clutter and the diverse shapes of firearms. To address this problem, we propose a novel approach that leverages human-firearm interaction information, which provides valuable clues for localizing firearm carriers. Our approach incorporates an attention mechanism that effectively distinguishes humans and firearms from the background by focusing on relevant areas. Additionally, we introduce a saliency-driven locality-preserving constraint to learn essential features while preserving foreground information in the input image. By combining these components, our approach achieves exceptional results on a newly proposed dataset. To handle inputs of varying sizes, we pass paired human-firearm instances with attention masks as channels through a deep network for feature computation, utilizing an adaptive average pooling layer. We extensively evaluate our approach against existing methods in human-object interaction detection and achieve significant results (AP=77.8\%) compared to the baseline approach (AP=63.1\%). This demonstrates the effectiveness of leveraging attention mechanisms and saliency-driven locality preservation for accurate human-firearm interaction detection. Our findings contribute to advancing the fields of security and surveillance, enabling more efficient firearm localization and identification in diverse scenarios.

Related papers

• Understanding Human Activity with Uncertainty Measure for Novelty in Graph Convolutional Networks [2.223052975765005]
  We introduce the Temporal Fusion Graph Convolutional Network. It aims to rectify the inadequate boundary estimation of individual actions within an activity stream. It also mitigates the issue of over-segmentation in the temporal dimension.
  arXiv  Detail & Related papers  (2024-10-10T13:44:18Z)
• AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System [52.93806509364342]
  This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. We introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features. We incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency.
  arXiv  Detail & Related papers  (2023-12-18T12:37:35Z)
• Attention Based Feature Fusion For Multi-Agent Collaborative Perception [4.120288148198388]
  We propose an intermediate collaborative perception solution in the form of a graph attention network (GAT) The proposed approach develops an attention-based aggregation strategy to fuse intermediate representations exchanged among multiple connected agents. This approach adaptively highlights important regions in the intermediate feature maps at both the channel and spatial levels, resulting in improved object detection precision.
  arXiv  Detail & Related papers  (2023-05-03T12:06:11Z)
• CCTV-Gun: Benchmarking Handgun Detection in CCTV Images [59.24281591714385]
  Gun violence is a critical security problem, and it is imperative for the computer vision community to develop effective gun detection algorithms. detecting guns in real-world CCTV images remains a challenging and under-explored task. We present a benchmark, called textbfCCTV-Gun, which addresses the challenges of detecting handguns in real-world CCTV images.
  arXiv  Detail & Related papers  (2023-03-19T16:17:35Z)
• PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search [56.02761592710612]
  We propose a novel attention-aware relation mixer (ARM) for module person search. Our ARM module is native and does not rely on fine-grained supervision or topological assumptions. Our PS-ARM achieves state-of-the-art performance on both datasets.
  arXiv  Detail & Related papers  (2022-10-07T10:04:12Z)
• ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection [102.9428507180728]
  A common problem in the task of human-object interaction (HOI) detection is that numerous HOI classes have only a small number of labeled examples. We observe that there exist natural correlations and anti-correlations among human-object interactions. We present techniques to learn these priors and leverage them for more effective training, especially on rare classes.
  arXiv  Detail & Related papers  (2021-09-09T06:02:50Z)
• Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control [28.463670610865837]
  We propose a distributed Reinforcement Learning (RL) approach that scales to larger swarms without modifications. Our experiments show that the proposed method can yield effective strategies, which are robust to communication channel impairments. We also show that our approach achieves better performance compared to a computationally intensive look-ahead.
  arXiv  Detail & Related papers  (2021-03-08T11:06:28Z)
• A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds [54.73161039445703]
  We propose a novel self-training approach that enables a typical object detector trained only with point-level annotations. During training, we utilize the available point annotations to supervise the estimation of the center points of objects. Experimental results show that our approach significantly outperforms state-of-the-art point-supervised methods under both detection and counting tasks.
  arXiv  Detail & Related papers  (2020-07-25T02:14:42Z)
• Localizing Firearm Carriers by Identifying Human-Object Pairs [12.425090880770977]
  In a given image, human and firearms are separately detected. Each detected human is paired with each detected firearm, allowing us to create a paired bounding box that contains both object and the human. A network is trained to classify these paired-bounding-boxes into human carrying the identified firearm or not.
  arXiv  Detail & Related papers  (2020-05-19T09:50:23Z)
• Towards High Performance Human Keypoint Detection [87.1034745775229]
  We find that context information plays an important role in reasoning human body configuration and invisible keypoints. Inspired by this, we propose a cascaded context mixer ( CCM) which efficiently integrates spatial and channel context information. To maximize CCM's representation capability, we develop a hard-negative person detection mining strategy and a joint-training strategy. We present several sub-pixel refinement techniques for postprocessing keypoint predictions to improve detection accuracy.
  arXiv  Detail & Related papers  (2020-02-03T02:24:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.