## 1 Introduction  
One of the key security issues during public events is how to accurately determine activity when people are moving and congregating. The congestion levels and anomaly detection are also extremely difficult tasks in video surveillance. With the expansion of human society activities, surveillance in recent years has become a very important and difficult task. In this work, we mainly focused on several events in indoor and outdoor environments while some events occurred in low light conditions and had shadows with large occlusions. An efficient crowd behaviour analysis is required to identify the abnormality of a crowd gathering and understand its behaviour by analysing video information in public surveillance videos. The analysed information is very useful to use for crowd management in public spaces based on predefined behaviours. However, in recent years, the technology of surveillance equipment has been significantly advanced in order to improve the quality of the obtained spatial information. Event spot analysis is still a difficult task due to the long viewing distance and the low-resolution quality of surveillance videos. The current approach to testing videos with a resolution of almost 299 \(\times\) 299 pixels with high occlusion allows a relatively low-quality video. Inprinciple, there are surveillance methods available in real time, leaving the analysis for later in case of a special investigation. However, both methods require a special interaction with a person to analyse the video, which can lead to visual fatigue and psychological weakness. Thus, the interaction with instances and a quick attention to unexpected situations, such as an abnormal event or a cluster of abnormal people, have a great impact on the task of video surveillance. Technically, an anomaly can be described as an unusual movement of a person, a cluster or a sudden convergence/divergence in a certain area of interest. In addition, crowd density level analysis measures the anomaly of traffic forecasting in surveillance video. The crowd analysis is mainly divided into several subsessions such as density, abnormal activity, crime scene, crowd counting, event understanding and many more. The group activity detection and event understanding need highly improved spatial-temporal model information to understand behaviours. The basic methods of all the crowd behaviour understanding are based on conventional spatial-temporal features such as handcrafted and convolution kernel features. Figure 1 shows the basic workflow of our proposed approach to understanding crowd behaviour in the public environment. The main contribution of the model provides an efficient deep learning feature extraction by modifying the existing conventional model to understand crowd congestion levels in public areas. The manually annotated congestion levels of information are described in Table 1 briefly. The congestion level in the scene is highly involved in understanding abnormal activity in the crowd. In this paper, we introduce a model to extract spatial information to detect basic activity and crowd density from attention-based feature classification with a multiclass CNN. There are several ways to understand the crowd density in a video sequence. In our approach, a sequence was divided into five congestion levels and tested with publicly available datasets described in the experiments and results section. The congestion levels were categorized from low to high as very low (VL), low (L), medium (M), high (H) and very high (VH). The proposed model was developed by fine-tuning of a densenet121 [1] and Efficientnetv2 [2] network and a multicolumn [3] CNN architecture to extract multichannel features from a single image instance. Figure 1: The network architecture of the proposed model. The bilinear CNN was based on the simultaneous extraction of different object features (different kernel size) from the same instance and at the same time, a multicolumn CNN was utilized to extract different filters to extract dense features from the same image.