# Real-Time Surveillance System for Analyzing Abnormal Behavior of Pedestrians
## 2 Related Work  
To analyze an abnormal behavior, we need to classify normal and abnormal event classes. Support vector machine (SVM) was an early stage machine learning algorithm to classify normal and abnormal behaviors. [PERSON] et al. proposed a method to detect an abnormal event using SVM based on the motion of the extracted region after removing redundant information by extracting a candidate region [5]. [PERSON] et al. used pyramid of histogram of oriented gradients (PHOG) to combine the shape and motion of the object with the histogram of the size, direction and entropy [6]. The extracted motion information is classified using SVM. To overcome the limitation of traditional machine learning techniques including SVM, motion estimation, and oriented gradient-based methods, a behavior recognition-based method recently used a two-stream network considering both temporal and spatial spaces [7]. [PERSON] proposed a two-stream action recognition network using both spatial information and optical flow [8]. To recognize motion information in more efficient manner, convolutional neural network-based multiple-stream networks were proposed in the literature [9; 10]. [PERSON] et al. proposed an self-estimation training method using a teacher-student method based on initially estimated optical flow, and [PERSON] et al. analyzed spatial information using convolutional neural network (CNN) and temporal information using Long Short-Term Memory (LSTM) [11; 12]. [PERSON] proposed Inflated 3D ConvNet (I3D) that can pre-train a 3D neural network by designing an extended network after training a 2D neural network for action recognition [13]. [PERSON] et al. takes the results of the I3D as inputs to four streams followed by normalization. For higher performance action recognition, feature vectors were normalized and then connected in [14]. Human pose information was additionally used for abnormal behavior analysis [15; 16; 17].  
Recently, various action recognition and detection methods were applied for visual surveillance systems [18; 19; 20; 21]. [PERSON] et al. proposed an anomaly detection system using sensors [21]. The sensor-based detection approach is more accurate than a single image-based method at the cost of additional sensors. [PERSON] et al. analyzed abnormal behavior using a tiny YOLO model implemented on an embedded board for commercial surveillance systems [19]. At the cost of the light-weight two-dimensional (2D) implementation, its behavior classification performance is lower than that of the 3D-CNN-based behavior recognition algorithm. [PERSON] et al. proposed a three-step method to analyze abnormal behavior using Faster Region-based Convolutional Neural Network (Faster R-CNN) [22],Histogram of Large Scale Optical Flow (HLSOF) descriptor, and Multiple Instance Support Vector Machine (MISVM) [20]. [PERSON]'s method analyzes abnormal behavior by efficiently characterizing action, but it does not have a system for efficiently managing crowd objects. To address the aforementioned issues, we propose an efficient abnormal behavior analysis method using a single image-based object management system. Since there are few approaches under low-light or low-resolution environments, it is not easy to get public datasets except KISA dataset that is optimized for CCTV environments including low-light and low-resolution environments. Most of the existing abnormal behavior studies evaluate the performance of behavior recognition algorithms using public datasets.