# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 5 Experimental Works
### Comparison with the State-of-the-Art
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Falling** & **Loitering** & **Violence** & **None** \\ \hline Recall & 0.98 & 0.93 & 0.97 & 0.93 \\ FP Rate & 0.01 & 0.02 & 0.01 & 0.02 \\ Precision & 0.98 & 0.93 & 0.97 & 0.93 \\ Accuracy & 0.99 & 0.97 & 0.98 & 0.96 \\ \(F_{1}\) & 0.98 & 0.93 & 0.97 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Final classification test results for 4 classes. for each dataset separately as well as for a combination of three datasets. In this way, their combined dataset setup is similar to our subset related to falling behavior on which they have reported a recall of 94% compared to the same of 98% in our case. As evident from the results in Table 8, our approach performs better than the state-of-the-art in terms of accuracy. [PERSON] et al. 's method performs better in terms of recall when tested on URF-DS and Multicam-DS individually. The higher value for precision, which refers to the fraction of predictions of falls that actually fell, suggests that the prediction performance is better than the state-of-the-art. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Paper** & **Pretraining** & **Dataset** & **Data Used** & **Method/Classification Approach** & **Precision** & **Recall** & **Accuracy** \\ \hline Zerrouki and & & & & Curvelet transforms and & & & \\ Houacine & - & URF-DS & RGB & & & & \\  & [65] & & & & area ratios features+ & - & - & 0.97 \\ \hline [PERSON] et al. & & & & & & & \\
[48] & - & Multicam-DS & RGB & & Histograms of Oriented & & \\  & [48] & - & Multicam-DS & RGB & & Gradients (HOG), LBP, & - & 0.93 & - \\ \hline [PERSON] et. al. & & & & & Caffe features+SVM & & & \\
[12] & - & Self-collected & RGB & GMM+2D-CNN & - & - & 0.90 \\ \hline \multirow{4}{*}{[PERSON] et al. [8]} & & & URF-DS & & & - & 1.00 & 0.95 \\ \cline{2-6}  & & & Multicam-DS & & & - & 0.99 & - \\ \cline{2-6}  & & UCF-101 & URF-DS+ & RGB, OF & & 2D-CNN & & & \\ \cline{1-1} \cline{2-6}  & & & Multicam-DS+ & & & & 0.94 & - \\ \cline{1-1} \cline{2-6}  & & & FDD & & & & & \\ \hline
**Proposed** & **Imagenet +** & **Kinetics** & **UFLV-DS** & **RGB, OF** & **2-Stream 3D-CNN** & **0.98** & **0.98** & **0.99** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with the state-of-the-art for falling. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper** & **Pretraining** & **Dataset** & **Data Used** & **Method/Classification Approach** & **Precision** & **Recall** & **Accuracy** \\ \hline \multirow{4}{*}{[PERSON] et al. [15]} & & & & Sequential & & & \\  & & & & micro-patterns+ & & & \\  & & & & Generalized Sequential & & & \\  & & & & Patterns (GSP) & & & \\  & & & & algorithm & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [14]} & & & & GMMs fusion, & & & & \\  & & & MeanShift+ & & & & \\ \cline{1-1} \cline{2-6}  & & PETS2007+ & & RGB & & activity area-based & 0.96 & 1.00 & 0.97 \\ \cline{1-1}  & & & & trajectory & & & \\ \cline{1-1}  & & & detection algorithm & & & & \\ \hline
**Proposed** & **Imagenet +** & & & & & & \\ \cline{1-1} \cline{2-6}
**Method** & **Kinetics** & **UFLV-DS** & **RGB, OF** & **2-Stream 3D-CNN** & **0.93** & **0.93** & **0.97** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison with the state-of-the-art for loitering. [PERSON] et al. (2015) have used the Generalized Sequential Patterns (GSP) algorithm to detect sequential micro-patterns in the input videos. This method maintains a database of frequently found loitering patterns and matches the newly found patterns with it for classification. They have reported the precision and recall values of 87% and 53%, respectively, on Caviar-DS. [PERSON] et al. (2014) have presented a method based on pedestrian activity area classification. They divided the loitering behaviors into three categories, namely rectangle, ellipse, and sector loitering. The proposed algorithms for each category calculate enclosing areas through curve fitting based on trajectory coordinates.