# Fast and Improved Real-Time Vehicle Anti-Tracking System
## 3 Implementation and Results
### Overall System Performance  
\(\bullet\) Occlusion  
For the relevant representation of the signature of a vehicle in the recognition phase, the SURF descriptor, which allows extracting characteristics invariant to scale and object rotation, is applied to detect points of interest of vehicles and to extract characteristics around these points. The SURF algorithm was used, but gave even better results because the training-image database only had vehicles stored in it. There was no background removal technique needed, nor cropping methods. This saves a great deal of time on the overall duration of the algorithm, which is what we wanted. The recognition system was very effective against the presence of occlusions and different viewing angles in the images; it was able to find a match with a very high threshold.  
\(\bullet\) External Conditions  
Each video is recorded in different places and in different scenarios. Part of the scenario suffers from a lack of accuracy. In the rainy scene (Video 5) various lights shine on the vehicle, and rain falls on the camera accidentally; due to unstable shots, owing to camera shake, the success rate decreased to 90%, which is still good. The resolution of this video sample is set to \(320\times 240\), and the duration is set to 180 s. Sample results for all of the subsystems are presented below (Table 1).  
Figure 18: Success rate of identification of the detected vehicle and and comparison with other methods: Canny edge [7], Cascade filters [10], Edge structure [16] and Background subtraction [17].  
\(\bullet\) The Shape of the Object  
Figure 19 shows that the system is effective for various categories of vehicles. We then examine the vehicle types' detection success rate. It is important to highlight that we did not work on vehicle classification. This is merely to see how effective our algorithm is when it needs to detect a car, truck, or bus. The results obtained are more or less what was expected. Normal-sized vehicles have a higher chance of being detected than the remaining types, because of their small size. For the training database, we used a large number of normal-sized vehicles (70%) compared with truck images (13%) and bus images (17%). Balancing the database will certainly increase the success rate of both trucks and buses; however, the results obtained are already more than satisfactory.  
Recognition results are a little less impressive compared with the detection because of the color dilemma. In one video, we had two identical vehicles with the same color and it was difficult to identify which vehicle was detected previously. The result gave a high threshold value for both, and of course one will be considered a false positive and the other a true positive, thus impacting the statistics.  
\(\bullet\) Real-Time Performance  
One of the most important aspects of our research has been the real-time element and the overall computation time of our system. Our system started showing some performance issues when we increased the frame rate of the videos. However, with a frame rate under 30 fps, the system gives excellent results, as shown in Figure 20.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video** & **Frame Rate (fps)** & **Total Vehicle** & \
\begin{tabular}{c} **Detection Success** \\ **Rate (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Tracking Success** \\ **Rate (\%)** \\ \end{tabular} \\ \hline Video 1 (Sunny) & 25 & 37 & 100 & 100 \\ Video 2 (Sunny) & 25 & 18 & 100 & 100 \\ Video 3 (Sunset) & 30 & 52 & 97 & 95 \\ Video 4 (Foggy) & 30 & 45 & 97 & 92 \\ Video 5 (Rainy) & 35 & 22 & 94 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall system performance.  
Figure 19: Success rate for various types of vehicle (true-positive rate) and comparison with other methods: Support vector machine [15], TBMOD [8] and Canny edge [7].