# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 5 Experimental Works
### Analysis of Detection Errors
#### 5.3.2 Missed Detections  
In the context of this study, a missed detection (false negative) refers to cases in which a stack of frames labeled as one of the abnormal behaviors i.e., falling, loitering, or violence is predicted as \"none\". As stated above, it is different from misclassification which is discussed in the next subsection. On analysis of samples, the following common sources of confusions leading to missed detections were found (see Table 3). 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 missed detections, and they do not reflect the number of detections missed for a class.  
Fall detection received the minimum false negatives (14.71%). Specifically, the following cases containing a fall were predicted as \"none\".  
_fn1_: the person tries not to fall while swinging during the walk before falling.  
_fn2_: 4.62% of the cases do not contain anything special. The system is possibly not learning a specific feature correctly to detect just a normal fall.  
_fn3_: the actor falls while trying to grab a nearby object (chair or table).  
Most of the missed detection cases were related to loitering (62.18%). This may be due to the fact that loitering contains the most indistinguishable features among the three abnormal behaviors which are mostly found in examples of none. The specific sources of errors observed for the missed detection of loitering are given in the following.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Missed Detection Cases** & **\% Cases Relative to Total Missed Detections** \\ \hline Fall predicted as None & 14.71 \\ \hline fr1 & 7.14 \\ \hline fr2 & 4.62 \\ \hline fn3 & 2.94 \\ \hline Loitering predicted as None & 62.18 \\ \hline ln1 & 31.09 \\ \hline ln2 & 21.43 \\ \hline ln3 & 9.66 \\ \hline Violence predicted as None & 19.33 \\ \hline vn1 & 8.82 \\ \hline vn2 & 5.88 \\ \hline vn3 & 4.62 \\ \hline Misc. cases & 3.78 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Percentage of different cases of missed detections.  
_In1_: 31.09% of cases do not contain anything special during loitering. These are just the normal cases of loitering that the system is unable to detect possibly because of learning some features incorrectly.  
_In2_: the scene contains many people, and one person is walking forward and backward repeatedly in the corridor. It can be hypothesized that the presence of several people (crowded scene) is resulting in the error.  
_In3_: the actor is sitting cross-legged next to the wall outside a shop.  
A total of 19.33% of cases of violence predicted as \"none\" contained the following common sources of errors.  
_vn1_: two players are fighting in a relatively distant scene; thus, the error may be related to the unclarity of features.  
_vn2_: several players are engaging in the fight.  
_vn3_: one of the two fighting players falls down.  
In addition to the above, some other rare cases of false negatives (various cases making a total of 3.78%) were also noted but they were insignificant and appeared only in a few stacks for each abnormal behavior.