# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 5 Experimental Works
### Analysis of Detection Errors
#### 5.3.1 False Alarms  
In the specific case of abnormal activity recognition, one can consider it a false alarm (false positive) when a stack of frames that has been labeled as \"none\" is predicted as one of the abnormal behaviors i.e., falling, loitering, or violence. Note that it is different from a misclassification in general, which in this case would mean, for instance, a falling predicted as violence. Misclassifications are discussed later in this section. A careful analysis of samples found the following common sources of confusion leading to false alarms in a majority of cases (see Table 2). It is important to note that the percentages given in the table only show the percentage of each case relative to the total number of false alarms made, and they do not reflect the number of false alarms made for a class.  
It was noticed that most of the false alarms (57.40%) were generated for falling. One possible reason for this may be that falling contains most of the patterns that are in some way similar to those used in videos for negative examples of each class. Specifically, the following cases belonging to \"none\" were predicted as falling.  
_nf_1: the person in the scene is slowly bending down until he reaches the floor and then lies down.  
_nf_2: the person in the scene bends down while sitting to pick an object from the floor.  
_nf_3: the actor enters or exits the scene walking slowly but unsteadily.  
_nf_4: the person in the scene is lying down.  
A total of 22.90% false positives were generated for violence. Specifically, the following cases containing no abnormal activity were predicted as fighting.  
_nv_1: two or more players are seen close to each other while at least one is lifting an arm.  
_nv_2: two or more players are close to each other and at least one bends down.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**False Alarm Cases** & **\% Cases Relative to Total False Alarms** \\ \hline None predicted as Fall & 57.40 \\ \hline nf1 & 23.40 \\ \hline nf2 & 16.00 \\ \hline nf3 & 11.40 \\ \hline nf4 & 6.60 \\ \hline None predicted as Violence & 22.90 \\ \hline nv1 & 10.90 \\ \hline nv2 & 6.40 \\ \hline nv3 & 5.60 \\ \hline None predicted as Loitering & 17.80 \\ \hline nl1 & 7.50 \\ \hline nl2 & 5.60 \\ \hline nl3 & 4.70 \\ \hline Misc. cases & 1.90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage of different cases of false alarms.  
_nv3_: several actors in the scene are performing normal acts involved in playing hockey such as running.  
Loitering received the minimum number of false alarms (17.80%). All cases in this category are related to the system erroneously ignoring the time perspective. Precisely, the following cases containing no abnormal activity were predicted as loitering.  
_nl1_: the person enters the scene walking steadily towards the exit, returns towards the entrance, and leaves the scene.  
_nl2_: the actor is working out.  
_nl3_: the actor is standing idly.  
In addition to the above, some other rare cases of false alarm (various cases making a total of 1.90%) were also noted but they were insignificant and appeared only in a few stacks for each abnormal behavior.