Mirzaei Behzad, Nezamabadi-Pour Hossein, Raoof Amir, Derakhshani Reza
Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran.
Department of Earth Sciences, Utrecht University, 3584CB Utrecht, The Netherlands.
Sensors (Basel). 2023 Aug 3;23(15):6887. doi: 10.3390/s23156887.
Object detection and tracking are vital in computer vision and visual surveillance, allowing for the detection, recognition, and subsequent tracking of objects within images or video sequences. These tasks underpin surveillance systems, facilitating automatic video annotation, identification of significant events, and detection of abnormal activities. However, detecting and tracking small objects introduce significant challenges within computer vision due to their subtle appearance and limited distinguishing features, which results in a scarcity of crucial information. This deficit complicates the tracking process, often leading to diminished efficiency and accuracy. To shed light on the intricacies of small object detection and tracking, we undertook a comprehensive review of the existing methods in this area, categorizing them from various perspectives. We also presented an overview of available datasets specifically curated for small object detection and tracking, aiming to inform and benefit future research in this domain. We further delineated the most widely used evaluation metrics for assessing the performance of small object detection and tracking techniques. Finally, we examined the present challenges within this field and discussed prospective future trends. By tackling these issues and leveraging upcoming trends, we aim to push forward the boundaries in small object detection and tracking, thereby augmenting the functionality of surveillance systems and broadening their real-world applicability.
目标检测与跟踪在计算机视觉和视觉监控中至关重要，它能够对图像或视频序列中的物体进行检测、识别以及后续跟踪。这些任务是监控系统的基础，有助于实现自动视频标注、重大事件识别以及异常活动检测。然而，由于小物体外观细微且具有有限的区分特征，在计算机视觉中检测和跟踪小物体带来了重大挑战，这导致关键信息匮乏。这种不足使跟踪过程变得复杂，常常导致效率和准确性降低。为了深入了解小物体检测与跟踪的复杂性，我们对该领域的现有方法进行了全面综述，并从多个角度对其进行了分类。我们还概述了专门为小物体检测与跟踪精心策划的可用数据集，旨在为该领域的未来研究提供信息并使其受益。我们进一步阐述了评估小物体检测与跟踪技术性能最广泛使用的评估指标。最后，我们审视了该领域当前面临的挑战，并讨论了未来可能的发展趋势。通过解决这些问题并利用未来趋势，我们旨在推动小物体检测与跟踪领域的发展边界，从而增强监控系统的功能并扩大其在现实世界中的适用性。
