Abstract
Object detection in videos plays a crucial role in advancing applications such as public safety and anomaly detection. Existing methods have explored different techniques, including CNN, deep learning, and Transformers, for object detection and video classification. However, detecting tiny objects, e.g., guns, in videos remains challenging due to their small scale and varying appearances in complex scenes. Moreover, existing video analysis models for classification or detection often perform poorly in real-world gun detection scenarios due to limited labeled video datasets for training. Thus, developing efficient methods for effectively capturing tiny object features and designing models capable of accurate gun detection in real-world videos is imperative. To address these challenges, we make three original contributions in this paper. First, we conduct an empirical study of several existing video classification and object detection methods to identify guns in videos. Our extensive analysis shows that these methods may not accurately detect guns in videos. Second, we propose a novel two-stage gun detection method. In stage 1, we train an image-augmented model to effectively classify ``Gun'' videos. To make the detection more precise and efficient, stage 2 employs an object detection model to locate the exact region of the gun within video frames for videos classified as ``Gun'' by stage 1. Third, our experimental results demonstrate that the proposed domain-specific method achieves significant performance improvements and enhances efficiency compared with existing techniques. We also discuss challenges and future research directions in gun detection tasks in computer vision.
Abstract (translated)
视频中的物体检测在推进公共安全和异常检测等应用方面起着关键作用。现有方法探索了包括卷积神经网络（CNN）、深度学习和Transformer在内的多种技术，用于对象检测和视频分类。然而，在视频中检测如枪支这样微小的物品仍然极具挑战性，因为它们的小尺寸及其在复杂场景中的多变外观导致难以准确识别。此外，现有的视频分析模型在进行分类或检测时，在真实的枪支检测场景下通常表现不佳，原因在于训练过程中使用的带标签视频数据集有限。因此，开发能够有效捕捉微小物体特征的方法，并设计出能够在真实世界视频中精确检测枪支的模型至关重要。 为了解决这些挑战，我们在本文中有三个原创贡献： 首先，我们对几种现有的视频分类和对象检测方法进行了经验研究，以识别它们在视频中定位枪支的有效性。我们的广泛分析显示，这些方法可能无法准确地检测出视频中的枪支。 其次，我们提出了一种新颖的两阶段枪支检测方法。第一阶段训练一个增强图像的数据模型来有效地将“含枪”和“不含枪”的视频进行分类。为了使检测更加精确高效，在第二阶段中，对于由第一阶段标记为"Gun"类别的视频，使用对象检测模型在视频帧内定位枪的确切区域。 第三，我们的实验结果表明，所提出的领域特定方法相比现有技术实现了显著的性能提升和效率增强。此外，我们还讨论了计算机视觉中的枪支检测任务面临的挑战及未来的研究方向。
URL
https://arxiv.org/abs/2503.06317
