The automatic detection of anomalies captured by surveillance settings is essential for speeding the otherwise laborious approach. To date, UCF Crime is the largest available dataset for automatic visual analysis of anomalies and consists of real-world crime scenes of various categories. In this paper, we introduce HR-Crime, a subset of the UCF-Crime dataset suitable and use a combination technique for human-related anomaly detection in real-time video surveillance system using pose-estimation and specified object tracking.