# An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders
## 4 Experimental Results and Comparisons
### Qualitative and Quantitative Results
Besides the obvious single anomaly, such as the bikers and the vehicles in (a), (b), (f), and (g), our method also works well in other complex scenes. In the scenes (c), (h), and (i), two abnormal events occur simultaneously. In (d), as our method can learn the appearance and motion feature representations, the skater that is almost the same as the pedestrian can also be detected. In (e) and (j), the anomalies are surrounded by normal pedestrians. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Ped1 (Frame)** & **Ped1 (Pixel)** & **Ped2** \\ \hline Adam [30] & 54.0\% & 19.5\% & - \\ MPPCA [31] & 59.0\% & 20.5\% & 69.3\% \\ SF [44] & 67.5\% & 19.7\% & 55.6\% \\ SF+MPPCA [32] & 66.8\% & 21.3\% & 61.3\% \\ MDT [32] & 81.8\% & 44.1\% & 82.9\% \\ SCLF [36] & 91.8\% & 63.8\% & - \\ AMDN [21] & 92.1\% & 67.2\% & 90.8\% \\ Sparse reconstruction [37] & 94.7\% & 64.0\% & - \\ Ours & **95.7\%** & **69.9\%** & **92.3\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Area under curve (AUC) comparison with the state of art methods. Figure 6: Equal error rate (EER) comparison of frame-level performance with different methods. Note that the performances of the sparse combination learning framework (SCLF) [36] and the sparse reconstruction [37] on Ped2 dataset are not available. Some failure cases that impede the performance of our method are reported in Figure 8. In Figure 8a, a man is walking along the street with nothing, but a box suddenly appears in his hand at the end of the sequence. Then our system wrongly detects him as an anomaly. In Ped1 sequence (b), a biker is almost the same as a pedestrian in some special angles, which causes our system to fail to detect it. In Ped2 sequence (c), part of the biker that appears at the bottom of the frame is correctly detected as an anomaly. However, for the biker that appears in the middle of the frame, our system misses it since the biker is similar to the grass background and the edge of the bike is too tiny to activate our system. In addition to UCSD Anomaly Detection Dataset, we also evaluate the performance of the proposed methods on the new color surveillance dataset: ShanghaiTech Campus dataset. We compare our anomaly detection method with two methods, Conv-AE [40] and FFP [6], since the dataset has not been widely used. The Area Under Curve (AUC) is cumulated to a scalar for performance evaluation. Following the work in [6], we leverage frame-level AUC for performance evaluation. The AUC of these two methods are taken from FFP [6] and listed with our method together in Table 2. For the 13 different scenes in the dataset, we trained their own models instead of only one model on all 13 scenes altogether like FFP did, because the definition of anomaly is different in these different scenes. We can see that our method outperforms the other two methods, which demonstrates the effectiveness of our method for colored scenes. Figure 8: Examples of failure results on Ped1 and Ped2 sequences. The detection results are marked with red color and the groundtruth are with green color. Figure 7: Examples of anomaly detection results on Ped1 (top) and Ped2 (bottom) sequences. The detection results are marked with red color and the groundtruth are with green color.