Department of Computer Graphics, Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland.
Polish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warszawa, Poland.
Sensors (Basel). 2020 Sep 2;20(17):4960. doi: 10.3390/s20174960.
Tracking and action-recognition algorithms are currently widely used in video surveillance, monitoring urban activities and in many other areas. Their development highly relies on benchmarking scenarios, which enable reliable evaluations/improvements of their efficiencies. Presently, benchmarking methods for tracking and action-recognition algorithms rely on manual annotation of video databases, prone to human errors, limited in size and time-consuming. Here, using gained experiences, an alternative benchmarking solution is presented, which employs methods and tools obtained from the computer-game domain to create simulated video data with automatic annotations. Presented approach highly outperforms existing solutions in the size of the data and variety of annotations possible to create. With proposed system, a potential user can generate a sequence of random images involving different times of day, weather conditions, and scenes for use in tracking evaluation. In the design of the proposed tool, the concept of crowd simulation is used and developed. The system is validated by comparisons to existing methods.
跟踪和动作识别算法目前广泛应用于视频监控、监测城市活动以及许多其他领域。它们的发展高度依赖于基准测试场景，这使得它们的效率可以得到可靠的评估/改进。目前，跟踪和动作识别算法的基准测试方法依赖于视频数据库的手动标注，容易出现人为错误，并且在规模和耗时方面存在限制。在这里，我们利用积累的经验，提出了一种替代的基准测试解决方案，该方案利用来自计算机游戏领域的方法和工具来创建具有自动标注的模拟视频数据。所提出的方法在数据规模和可创建的标注多样性方面都优于现有的解决方案。使用所提出的系统，潜在用户可以生成一系列涉及不同时间、天气条件和场景的随机图像，用于跟踪评估。在提出的工具的设计中，使用并开发了人群模拟的概念。该系统通过与现有方法的比较进行了验证。
