# Temporal Saliency-Based Suspicious Behavior Pattern Detection
## 3 Results and Discussion
### Comparison Results from Experiments with UMN Dataset and Avenue Dataset  
For the UMN dataset, the method based on optical flow-based features [8], Bayesian model [25], chaotic invariants [11], the social force model [10], and sparse reconstruction cost [12] were compared with the proposed method. The UMN dataset contains three crowd escaping scenes in both indoor and outdoor environments. The normal events depict people wandering in groups, while abnormal events depict a crowd escaping quickly. The dataset contains \(11\) video sequences that are captured in three different backgrounds. Scene 1 and Scene 3 are outdoor scenes (lawn, plaza) and Scene 2 is an indoor scene. The accuracy was defined to be the percentage of correctly identified frames that are calculated by comparing with ground truth. Table 2 demonstrates the accuracy comparison of six methods for three different scenes of the UMN dataset in identifying escape events. Figure 7: Example result of calculating temporal saliency map. (**a**) original frame; (**b**) result of the system; (**c**) \(R_{\text{mag}}\); (**d**) \(R_{\text{grad}}\); (**e**) temporal saliency map (\(T\)S). The methods in [8; 25] were previously tested on the whole UMN dataset, and the provided results were used to compute the corresponding accuracy. As an evaluation setting, the same evaluation settings as described in [25] were used. Overall, the proposed method achieves the best accuracy with an average of 96.50%, which is higher than that of the other methods. Even though the proposed method did not employ any learning process, the proposed method outperforms the comparison methods. Such methods using the learning process are dependent on the training dataset and also require lots of computation, so it is hard to be used as a real-time surveillance system. The proposed method can be used in real life by supplementing these matters. Figures 8-10 demonstrate the examples of detection results for abnormality from the UMN dataset (No. 1 to No. 3) in the proposed method. In these videos, the suspicious behavior is the sudden movement of people. When people run away in multiple directions at the same time, the direction of movement appears very irregular and the size of the movement is also dramatically increased. The proposed method responded appropriately to this kind of motion. The area in which people are running away has been properly detected. In the scene where several people are running away, we can see that the motion vector is greatly increased, and all of the suspicious behavior is detected. The proposed method generated reactivity images using feature information extracted from optical flow in the video and detected anomalous regions based on temporal saliency obtained through a weighted combination of them. Feature information using the magnitude and gradient of movement, which is the most important factor that constitutes a behavior, is extracted, and a strongly reactive region is detected through a weighting condition formula. The result demonstrates that the suspicious behavior was reasonably detected. For the Avenue dataset, the methods described in [21; 26; 29] were compared with the proposed method. The Avenue dataset contains 16 training videos and 21 testing videos. The only normal behavior in the dataset is people walking in front of the camera, and the abnormal behaviors are unusual actions such as running and jumping, and walking in the wrong direction. Table 3 demonstrates the AUC (area under the curve) values of both the proposed method and the state-of-the-art comparison methods [21; 26; 29] for the Avenue dataset. The method in [26] was previously tested on the whole Avenue dataset, and the provided results were used in the comparison. The comparison results of Table 4 shows that the performance of the proposed method outperforms the comparison methods.