# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 2 Related Work  
Crowd anomaly detection aims to detect changes in and automatically identify crowd events in video sequences [3; 4]. Currently, two main types of techniques are used in crowd anomaly detection: object-based and holistic techniques [5]. Object-based techniques require the breaking down of the crowd into smaller groups and the analysis of their extracted trajectories to infer crowd behaviour [6; 7]. However, these methods struggle to recognise activities within a crowded scene due to occlusion and the loss of target object visibility. On the other hand, holistic approaches view the crowd as a single interconnected system and focus on exploiting low- and medium-level features to analyse crowd behaviours [8]. Optical flow fields are often used in these methods [9; 10; 11; 12], which can detect various crowd events. [PERSON] and [PERSON] [9] introduced an automated method that utilises optical flow histograms to represent the overall motion of a crowd in a scene. These histograms were used to identify potentially hazardous situations within crowds, such as the Love Parade stampede in Germany in 2010. [PERSON] et al. [10] utilised low-level motion features to create crowd models for direction and magnitude. Then, they generated unique motion sequences using a segmentation algorithm based on regions, which enabled the detection of various crowd events. [PERSON] et al. [12] proposed a Riemannian probabilistic detection framework based on optical flow manifolds to detect various crowd events, such as running, walking, and local dispersion. Newer methods go beyond the frame-to-frame motion information used by earlier works by either tracking particularly interesting points [13; 14; 15] or employing particle advection [16; 17; 18]. These methods use trajectory information to capture long-term temporal dependencies and extract motion patterns for crowd anomaly detection. [PERSON] et al. [13] proposed the histogram of oriented tracklets (HOT), a 2D histogram-based motion descriptor that encodes both the magnitude and direction of motion. It helps to spot unusual events in densely populated scenes. CAD has been an active area of research in recent years, with various techniques and approaches being proposed to overcome the various challenges reported in the literature. A review of the recent advancements in crowd anomaly detection was presented in [2; 19], which discussed the integration of multiple modalities and addressed challenges such as occlusion and scale variation. This paper concludes with a discussion of future research directions in crowd anomaly detection. A detailed review of the anomaly detection methods in crowd scenes from the computer vision perspective was presented in [1], which focused on studying the human crowd, specifically abnormal human behaviour. The paper summarised the state-of-the-art anomaly detection methods in crowd scenes and categorisedthem based on their approach, anomaly scope, and processing target. It highlights the importance of intelligent monitoring systems for effective crowd management and discusses the role of computer vision, video analysis, and automated crowd anomaly detection in this context. Crowd anomaly detection using spatial constraints and meaningful perturbation was proposed in [20], which addressed the challenges of rare and diverse abnormal events in crowd scenes. The paper focused on detecting anomalies in crowded scenes to enhance automatic video surveillance systems. A novel approach to crowd anomaly detection was proposed in [21], which focused on detecting anomalies in crowded scenes to enhance automatic video surveillance systems. The proposed method used a combination of multiple optimised convolutional neural networks (ConvNets) to detect anomalies in video data showing crowded scenes. The approach was designed to be efficient and have a low computational cost, making it suitable for real-time applications. In [22], abnormal behaviours were detected in a two-step process.