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\title{Computer Vision based Traffic Monitoring and Analyzing from On-Road Videos}
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             \author[1]{Md. Shamim Reza  Sajib}

             \author[2]{T.M. Amir-Ul-Haque  Bhuiyan}

             \affil[1]{  Bangladesh University of Business and Technology}

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\date{\small \em Received: 7 December 2018 Accepted: 5 January 2019 Published: 15 January 2019}

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\begin{abstract}
        


Traffic monitoring and traffic analysis is much needed to ensure a modern and convenient traffic system. However, it is a very challenging task as the traffic condition is dynamic which makes it quite impossible to maintain the traffic through traditional way. Designing a smart traffic system is also inevitable for the big and busy cities. In this paper, we propose a vision based traffic monitoring system that will help to maintain the traffic system smartly. We also generate an analysis of the traffic for a certain period, which will be helpful to design a smart and feasible traffic system for a busy city. In the proposed method, we use Haar feature based Adaboost classifier to detect vehicles from a video. We also count the number of vehicles appeared in the video utilizing two virtual detection lines (VDL). Detecting and counting vehicles by proposed method will provide an easy and cost effective solution for fruitful and operative traffic monitoring system along with information to design an efficient traffic model.

\end{abstract}


\keywords{video based traffic monitoring, traffic surveillance, counting vehicles, traffic model.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
ision based traffic surveillance system has become an active area of research interest over the past decade in the developed countries. This is a potential area of research as it has some significant applications. In Bangladesh traffic jam is very common an unpredictable. Due to limitation of work force, authorities are unable to find the reason and control this. It is of interest to digitally process and analyze these videos in real-time in order to extract reliable data on traffic flow and to detect traffic events. For example, because of such video analysis, traffic density in major arteries can be estimated and the least congested routes and travel time estimates can be computed. This information can be achieved by counting vehicles passing through the roads. By implementing this, the authority can have the information of traffic flow on a particular road on a particular time, measure rush and reduce the problem. This type of traffic surveillance system is becoming popular in many countries.\par
The proposed method needs two major things to be implemented: i) Detecting and counting vehicles, ii) Generate necessary information to be used to maintain the traffic. Many approaches have been introduced for vehicle detection. Some available methods uses lidar, radar and computer vision. As camera is cheaper than radar or lidar, vision based vehicle detection and classification has become more popular than lidar or radar based detection system. Though computational power has increased dramatically, vehicle detection and classification is not an easy task. The problem is the dynamic environment of the road. The condition of the road cannot be predicted. There can be many human made infrastructures; pedestrians, which makes this, task a difficult one. In addition, there are change in background, illusion and heterogeneity of vehicles. 
\section[{II.}]{II.} 
\section[{Related Works}]{Related Works}\par
Background subtraction based method is used by \hyperref[b0]{[1]} for detection. Deep neural network based detection and classification model \hyperref[b1]{[2]} is very expensive in terms of computational resources and time and is not suitable in real time. Niluthpalet. al. \hyperref[b2]{[3]} generates time spatial images from video frames and gain a very good speed and accuracy in detection. Vehicle detection approaches can be divided into two broad categories: appearance based and motion based methods.\par
Camera placement plays a significant role in video-based vehicle detection. Camera can be moving or static. As occlusion is the main problem in vision based detection system, camera should be placed in some position that minimizes the probability of occlusion. Camera placement depends on the appearance of the vehicles. In \hyperref[b2]{[3]} camera is placed in an over bridge for taking both incoming and outgoing vehicles. It takes both front and rear view of vehicles. Broggi et al. \hyperref[b3]{[4]} placed a camera to capture the side view of vehicles. Sivram and Trevedi \hyperref[b4]{[5]} mounted camera in front of the moving vehicles that capture the rear view of front side vehicles. For static camera, good choice is some higher position than the level of vehicles that reduces the chances of occlusion. This decreases the chance of partial occlusion caused by vehicles but vehicles appearance changes from first lane to third len or fourth len. Yong Tang \hyperref[b5]{[6]} placed the camera in a high position like over bridge and capture the front view of the vehicles\par
To detect vehicles from a frame, features are extracted from the frames. Many types of features have been introduced for vehicle detection so far. Sivram and Trevedi \hyperref[b6]{[7]} used edge features to highlight the side of a vehicle and cast shadow. In recent years, some strong features due to the robustness and reliability replace simple features like edges or corners. These features are commonly used in computer vision for both detection and classification. Histogram of oriented gradient (HOG) was extremely well represented for vehicle detection as well as object detection. Inspired by human detection method \hyperref[b7]{[8]} of Dalal and Triggs \hyperref[b8]{[9]} has used HOG features nicely to detect vehicles. At first they compute gradients from the images and extract HOG features. A linear SVM classifier trains the extracted features. Though HOG features provide a very good detection rate, the main drawback is its calculation speed. As classification will be done after detection, its speed should be good. Haar-like features have been used nicely for face detection. Haar-like features are calculated with the help of integral image. Integral image can be calculated at a very fast speed. Due to its calculation speed and successful use in face detection, it has been also used for vehicle detection in \hyperref[b9]{[10]} and \hyperref[b10]{[11]} successfully. Scale invariant feature transform (SIFT) \hyperref[b11]{[12]} was used in \hyperref[b12]{[13]} to detect rear faces of vehicles. Though this feature cannot provide better performance than HOG or Haar, it is considerably good in case of occlusion. Lin et al. \hyperref[b13]{[14]} used a combination of SURF \hyperref[b14]{[15]} and edge features to detect vehicles in the blind spot.\par
Support vector machine (SVM) is a strong binary classifier. It has been widely used for vehicle detection. The combination of HOG features and SVM classifier have been used for vehicle detection in \hyperref[b8]{[9]} and \hyperref[b15]{[16]}.\par
AdaBoost \hyperref[b16]{[17]} is also widely used in real time vehicle detection. As the classification speed of AdaBoost classifiers is high, it has become popular in real time classification applications. A combination of Haar-like features and AdaBoost classification is used in \hyperref[b17]{[18]} and \hyperref[b18]{[19]} for vehicle's rear face detection and perform very good in real time. The purpose of the AdaBoost algorithm is to use the feature to discover the best weak classifiers to form a strong classifier, and has shown its capability to improve the performance of various detection and classification applications. Actually the strong classifier is an ensemble classifier composed of many weak classifiers that just better than a random guess. Tang et al. \hyperref[b5]{[6]} also used this method successfully. This method is very fast and provide high accuracy. However, the main drawback of this method is high false positive rate. The method proposed in \hyperref[b17]{[18]} and \hyperref[b18]{[19]} achieve a accuracy of 98\% with 3\%-5\% false positive rate which is not tolerable in these type of applications. Yong Tang \hyperref[b5]{[6]} used this method and achieved good accuracy but had a false positive rate of 3\%. This method is very fast and applicable in real time. But false positive rate can cause inapplicable in some sectors.\par
In recent year deep neural network and model, based classification is being used to detect 3D vehicles.\par
Both need high computational resources and execution time. Researchers have done some motion-based approaches. In \hyperref[b3]{[4]}, an adaptive background model was constructed, with vehicles detected based on motion that differentiated them from the background. Adaptive background modeling was also used in \hyperref[b19]{[20]}, especially to model the area where overtaking vehicles tend to appear in the cameras end of view. In \hyperref[b2]{[3]} they used three virtual detection lines and generate time spatial image (TSI) from three frames. The vehicles present in a time spatial image is called TSI object blobs (TOBs). Then canny edges of TOB is generated. After that binary masks of the TOBs are obtained. Then vehicles are detected from multiple TOBs. This method generates a very good result with a good calculation speed and applicable in real time application. However, it is not suitable in conditions where there is heavy rush on the road and vehicles are moving in a low speed.\par
After detecting the vehicles, we count the total number of vehicles. Bas, Erhan and Tekalp \hyperref[b20]{[21]} proposed adaptive background subtraction and Kalman filtering based model to count vehicle. However, their method provides a very good accuracy but it is comparatively slow to compute. Unzueta, Luis, et al. \hyperref[b21]{[22]} propose a robust adaptive multi cue segmentation strategy that detects foreground pixels corresponding to moving and stopped vehicles, even with noisy images due to compression. This method is also reliable and provides satisfactory result in various conditions. 
\section[{III.}]{III.} 
\section[{Proposed Method}]{Proposed Method}\par
Our method is proposed targeting real time applications. Therefore, the execution time must be faster in both detection, counting and analysis. We are proposing a cost effective and faster model that can easily be implemented for real time traffic surveillance. In the next sub sections, we will discuss about the approaches and methods we use for detection and counting vehicles from a video and then generate the information about traffic. 
\section[{a) Detection}]{a) Detection} 
\section[{i.}]{i.}\par
Camera placement: At the very first stage of detection, we have to select a suitable place to set camera. We propose our method targeting to run on videos taken by some static camera. ii.\par
Feature Extraction: As we propose our method for real time application, we need so select a feature that is fast to compute. In proposed method, haar-like features are chosen for vehicle detection.\par
The detection is done using Viola-Jones object detection framework. A window of the target size is moved over the input image, and for each subsection of the image, the Haarlike feature is calculated. Sub-window that remains positive after going through some steps are said to be positive and the area covering by some joined sub-window is detected as vehicles. 
\section[{b) Counting}]{b) Counting}\par
As a vehicle can appear in multiple frames, it will be detected multiple times also. However, we have to count only once. Therefore, we cannot count each vehicle of all the frames. To count a vehicle only once, we use two virtual detection lines (VDL). The classifier only computes the region of the virtual detection line. So when vehicles appear on the region within virtual detection lines, only then they are detected. When a vehicle appears in the VDL its full rear view must be seen and for this no chance of false detection. As a vehicle must appear within the region of VDL, there will be no possibility to miss any vehicle. Still there is possibility of counting a vehicle more than once. Therefore, we count vehicle after each three frames. The length of the VDL is set in such a way that no vehicle can go past the VDL within these three frames. The difference of the two VDL is the length a vehicle can cover within five frames at an average of 40km/h. Therefore, the faster vehicle like bus or car cannot disappear within five frames and the slower vehicle like rickshaw won't appear twice within the frame as we count after each three frames. This provides a very good accuracy in counting. 
\section[{c) Classifying Road Condition}]{c) Classifying Road Condition}\par
After counting the vehicles passing through a road, we generate some useful information that can be used to operate traffic smartly and design an efficient traffic model. We experiment the proposed method in the videos taken from a road then we count the number of vehicles passing through that road in a certain amount of time. Suppose there is a busy road where a large number of vehicles go through within 9.00 am to 9.30 am. However, this road can be free at another time. We count the number of vehicles for every 20 minutes. According to that number and the width of the road, we classify the condition of the road. We make four categories of the condition of the road i) free ii) moderate iii) busy and iv) heavy. We generate ratio of vehicle and the lane by following equation.\par
The value number of vehicles is found by counting vehicles in every 20 minutes. If the density is less than or equal 200, then we say the condition of the road is free. If it is between 200 and 300 then the condition of the road is moderate. If it is between 300 and 400 then the road is busy and after that, the condition of the traffic of the road is heavy.   
\section[{Experimental Result}]{Experimental Result}\par
Multiple experiments have been done to generate the result of the method. All the experiments have been done on different videos. 
\section[{a) Data Set Collection}]{a) Data Set Collection}\par
There are 15 videos taken from Kalshi Road, Mirpur, Dhaka, Bangladesh from the side of the roads. Videos are taken on different environments including sunny, cloudy weather in different times. Some videos are taken in and opposite direction of sunlight.\par
Another 10 videos taken by MVDL \hyperref[b2]{[3]} authors is used to test the proposed method.\par
For training we take two instances of each image thus the positive image set for detection contains almost 5000 images of vehicles. For negative image set, we use 1300 negative images of Caltech car data set and 700 images of local roads. The negative image set contains almost 2000 images. Those images are resized and used for training. 
\section[{b) Result}]{b) Result}\par
Extensive experiments have been carried out to generate the result of the proposed method. Without considering occlusion, the average detection rate is 97.81\%. Average false positive rate is reduced to 1.8\%.\par
The accuracy of counting is also satisfactory. The proposed method provides 93.11\% counting accuracy. The Roads where After counting the vehicles for a particular time (20 minutes), the proposed method provides the information about the condition of the roads. The results are generated by calculating the density. We have carried out experiments in our collected videos taken from different place of Dhaka city. We find that the method provides accurate result about the condition of the roads.\par
V. 
\section[{Conclusion}]{Conclusion}\par
Traffic monitoring by analysing videos of the road is a very challenging task. It is more difficult on the busy roads like \begin{figure}[htbp]
\noindent\textbf{12}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Fig. 1 :Fig. 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{34}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Fig. 3 :Fig. 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Fig. 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{I} \par 
\begin{longtable}{P{0.41884057971014493\textwidth}P{0.4311594202898551\textwidth}}
Density\tabcellsep Road Condition\\
0-200\tabcellsep Free\\
201-300\tabcellsep Moderate\\
301-400\tabcellsep Busy\\
401-Rest\tabcellsep Heavy\end{longtable} \par
 
\caption{\label{tab_0}Table I :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{II} \par 
\begin{longtable}{P{0.09366940211019929\textwidth}P{0.09566236811254397\textwidth}P{0.2979484173505276\textwidth}P{0.029894490035169988\textwidth}P{0.15345838218053928\textwidth}P{0.022919109026963657\textwidth}P{0.0637749120750293\textwidth}P{0.09267291910902696\textwidth}}
Year 2 019\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
G ( ) Volume XIX Issue II Version I\tabcellsep Traffic Extract Frame from a Video Analyze the Traffic Condition by Computing the Density of the\tabcellsep \multicolumn{2}{l}{Detect Vehicles Using Haar Feature based AdaBoost Classifier Count Vehicle Using VDLs}\tabcellsep \tabcellsep \\
Global Journal of Computer Science and Technology\tabcellsep \tabcellsep Place Kalshi Road, Mirpur, Dhaka Kalshi Road, Mirpur, Dhaka Kalshi Road, Mirpur, Dhaka Kalshi Road, Mirpur, Dhaka Cantonment Fly Over Road, Dhaka Shahbagh, Dhaka Shahbagh, Dhaka\tabcellsep No. of Videos 4 4 5 2 3 5 3\tabcellsep \multicolumn{2}{l}{Position Side Side Side Side Side 11.00 AM Time 9.00 AM 12.00 PM 4.00 PM 2.00 PM Over Bridge 10.00 AM Over Bridge 2.00 PM}\tabcellsep Duration (Mins) 6.36 6.23 9.25 5.17 3.11 22.54 12.21\tabcellsep Environment Sunny Cloudy Sunny Sunny Against Sun-light Sunny Cloudy\\
\tabcellsep \tabcellsep Shahbagh, Dhaka\tabcellsep 2\tabcellsep Over Bridge\tabcellsep 4.00 PM\tabcellsep 9.46\tabcellsep Partially Sunny\\
\tabcellsep \tabcellsep BUET, Dhaka\tabcellsep 2\tabcellsep Over Bridge\tabcellsep 10.00 PM\tabcellsep 8.35\tabcellsep Sunny\\
\tabcellsep \tabcellsep BUET, Dhaka\tabcellsep 2\tabcellsep Over Bridge\tabcellsep 12.00 PM\tabcellsep 4.49\tabcellsep Cloudy\end{longtable} \par
 
\caption{\label{tab_1}Table II :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{III} \par 
\begin{longtable}{P{0.7067415730337079\textwidth}P{0.07449438202247191\textwidth}P{0.06876404494382021\textwidth}}
Data Set\tabcellsep Detection Accuracy\tabcellsep False Positive Rate\\
Data Set 1\tabcellsep 97.81\%\tabcellsep 1.25\%\\
MVDL [3](Data Set 2)\tabcellsep 97.23\%\tabcellsep 1.67\%\\
Zhang [1]\tabcellsep 97.37\%\tabcellsep 2.5\%\\
\multicolumn{3}{l}{rickshaws are plenty, there the accuracy of the method}\\
\multicolumn{3}{l}{decreases. The rickshaws are very slow compared to}\\
\multicolumn{3}{l}{others vehicle and for this one rickshaw may appear}\\
\multicolumn{3}{l}{twice in the VDL region. We can see the result of the}\\
\multicolumn{3}{l}{video taken from BUET. Most of the vehicles there are}\\
\multicolumn{3}{l}{rickshaws and the total count is greater than the}\\
actual count.\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table III :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{IV} \par 
\begin{longtable}{P{0.24741784037558684\textwidth}P{0.1755868544600939\textwidth}P{0.10375586854460093\textwidth}P{0.1516431924882629\textwidth}P{0.17159624413145538\textwidth}}
Road Name\tabcellsep Duration of Video (Mins)\tabcellsep Actual Count\tabcellsep Count by Method Proposed\tabcellsep Accuracy\\
Kalshi Road\tabcellsep 6.23\tabcellsep 288\tabcellsep 270\tabcellsep 93.75\%\\
Kalshi Road\tabcellsep 9.25\tabcellsep 396\tabcellsep 375\tabcellsep 94.70\%\\
Matikata Overbridge\tabcellsep 3.23\tabcellsep 114\tabcellsep 105\tabcellsep 92.10\%\\
Shahbagh\tabcellsep 5.52\tabcellsep 244\tabcellsep 231\tabcellsep 94.64\%\\
BUET\tabcellsep 4.49\tabcellsep 87\tabcellsep 96\tabcellsep 89.65\%\end{longtable} \par
 
\caption{\label{tab_3}Table IV :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{V} \par 
\begin{longtable}{P{0.27101449275362316\textwidth}P{0.18478260869565216\textwidth}P{0.1683574879227053\textwidth}P{0.22584541062801933\textwidth}}
\tabcellsep \tabcellsep \tabcellsep the roads of\\
Road Name\tabcellsep Duration of Video (Mins)\tabcellsep Time\tabcellsep Traffic Condition\\
Kalshi Road\tabcellsep 6.23\tabcellsep 12.00 PM\tabcellsep Moderate\\
Kalshi Road\tabcellsep 20.21\tabcellsep 9.00 AM\tabcellsep Heavy\\
Matikata\tabcellsep \tabcellsep \tabcellsep \\
Over-\tabcellsep 3.23\tabcellsep 12.00 PM\tabcellsep Free\\
bridge\tabcellsep \tabcellsep \tabcellsep \\
Shahbagh\tabcellsep 5.52\tabcellsep 2.00 PM\tabcellsep Busy\\
Shahbagh\tabcellsep 5.52\tabcellsep 4.00 PM\tabcellsep Heavy\end{longtable} \par
 
\caption{\label{tab_4}Table V :}\end{figure}
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