# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 5 Experimental Works
### Analysis of Detection Errors
#### 5.3.3 Misclassifications  
This section discusses the cases where the confusion due to the inherent problem of joint detection discussed in Section 3.4 resulted in classification errors. On analysis of samples, for a majority of cases, it was possible to identify the common sources of confusion as follows (see Table 4). It is important to mention that the percentages given in the table only show the percentage of each case relative to the total number of misclassifications and not the percentage of misclassifications done by the system. A total of 2.24% cases of fall predicted as loitering involved the following common sources of errors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Misclassification Cases** & **\% Cases Relative to Total Misclassifications** \\ \hline Fall predicted as Loitering & 2.24 \\ \hline fl1 & 1.40 \\ \hline fl2 & 0.84 \\ \hline Fall predicted as Violence & 19.33 \\ \hline fv1 & 9.80 \\ \hline fv2 & 6.16 \\ \hline fv3 & 3.36 \\ \hline Loitering predicted as Fall & 6.72 \\ \hline lf1 & 4.20 \\ \hline lf2 & 2.52 \\ \hline Loitering predicted as Violence & 36.69 \\ \hline lv1 & 18.21 \\ \hline lv2 & 12.04 \\ \hline lv3 & 6.44 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentage of different cases of misclassifications.  
_f1_: 1.40% of the cases do not contain anything special about them.  
_f1_: the actor spends at least 2 s walking normally before swinging and falling.  
A total of 19.33% of cases of fall predicted as violence involved the following common sources of errors.  
_fv1_: the actor tries to balance himself by raising arms sideways before the fall.  
_fv2_: the actor walking unsteadily and falls sideward.  
_fv3_: the actor falls while raising a hand in which he is holding a ball.  
A total of 6.72% of cases of loitering predicted as fall involved the following common sources of errors.  
_f1_: the actor stretches arms overhead while walking straight.  
_f2_: the actor walks unsteadily for a minimum of 5 s before bending down to pick an object from the floor.  
A total of 36.69% of cases of loitering predicted as violence involved the following common sources of errors.  
_lv1_: the actor is walking fast while moving arms swiftly.  
_lv2_: the actor is walking normally, holding an object (stick, handbag) in hand.  
_lv3_: the actor walks for a minimum of 5 s, crouches down, and continues walking normally.  
A total of 20.45% of cases of violence predicted as fall involved the following common sources of errors. Overall, with respect to actions or movement patterns, these cases are similar to those of fall predicted as violence. Similarly, a total of 12.89% cases of violence predicted as loitering involved the following common sources of errors. A majority of movement patterns in this class resemble those listed above for loitering predicted as violence. In addition to the above, other random cases of misclassifications (making a total of 1.68%) were also noted but they were insignificant and appeared only in a few stacks for each abnormal behavior.