Highway Traffic Videos Research Articles

Road Traffic Analysis Using Computer Vision

Abstract: Traffic analysis plays an important role in a transportation system for traffic management. Traffic analysis system using computer vision project paper proposes the video based data for vehicle detection and counting systems based on the computer vision. In most Transportation Systems cameras are installed in fixed locations. Vehicle detection is the most important requirement in traffic analysis part. Vehicle detection, tracking, classification and counting is very useful for people and government for traffic flow, highway monitoring, traffic planning. Vehicle analysis will supply with information about traffic flow, traffic summit times on road. The motivation of visual object detection is to track the vehicle position and then tracking in successive frames is to detect and connect target vehicles for frames. Recognising vehicles in an ongoing video is useful for traffic analysis. Recognizing what kind of vehicle in an ongoing video is helpful for traffic analysing. this system can classify the vehicle into bicycle, bus, truck, car and motorcycle. In this system I have used a video-based vehicle counting method in a highway traffic video capture using cctv camera. Project presents the analysis of tracking-by-detection approach which includes detection by YOLO(You Only Look Once) and tracking by SORT(simple online and realtime tracking) algorithm. Keywords: Vehicle detection, Vehicle tracking, Vehicle counting, YOLO, SORT, Analysis, Kalman filter, Hungarian algorithm.

Fast approach for moving vehicle localization and bounding box estimation in highway traffic videos

Detection and accurate localization of position of moving vehicles in a video sequence are the primary tasks in every computer vision-based traffic monitoring system. In this paper, a moving vehicle localization and bounding box estimation algorithm is proposed. The moving vehicle foreground is separated from the static background by the adaptive background subtraction method, and the bounding box of moving vehicles is estimated with two-dimensional binary histogram projection profile (2D-BHPP) algorithm. The foreground object refinement is performed by means of morphological closing operation prior to apply the 2D-BHPP algorithm. The proposed method only computes the four necessary minimum bounding box coordinates, i.e., left, right, upper, and lower of moving vehicles in every frame. The proposed method is tested over three publicly available data sets, and the results show that the localization algorithm works comparatively faster and accurately than the existing localization methods. The detection error rate, IoU metric, and execution time per frame signify that this approach can be implemented in edge-based real-time applications.

Pixel matching search algorithm for counting moving vehicle in highway traffic videos

Traffic monitoring through video processing is one of the hot research areas in the Intelligent Transportation System (ITS). Vehicle counting systems should be simple enough to be applied in real-time circumstances. A novel and fast algorithm for vehicle counting from a traffic video sequence is proposed in this paper where the vehicle tracking step is not necessary. A reference model is only created in the video frames for a narrow area. When going through this narrow area, the moving vehicles are identified as foreground objects. Detection of moving vehicles is achieved by integrating approximated median filter based background subtraction with binary integral projection. The detected candidates are counted as a vehicle using a novel pixel matching search algorithm. The proposed algorithm does not rely on every video frame. It only requires every third frame for processing and thus increases the computation speed by three times compared to existing techniques. The proposed algorithm is tested and validated on a standard data set as well as a custom data set. Two parameters such as accuracy and processing time are used for the system evaluation where an overall accuracy of 96.84% is achieved. The processing time results show that the proposed system can perform in real-time with an average real-time processing speed of 93.92%.

Localized LRR on Grassmann Manifold: An Extrinsic View

Subspace data representation has recently become a common practice in many computer vision tasks. Low-rank representation (LRR) is one of the most successful models for clustering vectorial data according to their subspace structures. This paper explores the possibility of extending LRR for subspace data on Grassmann manifold. Rather than directly embedding the Grassmann manifold into the symmetric matrix space, an extrinsic view is taken to build the self-representation in the local area of the tangent space at each Grassmannian point, resulting in a localized LRR method on Grassmann manifold. A novel algorithm for solving the proposed model is investigated and implemented. The performance of the new clustering algorithm is assessed through experiments on several real-world data sets including MNIST handwritten digits, ballet video clips, SKIG action clips, and DynTex++ data set and highway traffic video clips. The experimental results show that the new method outperforms a number of state-of-the-art clustering methods.

Background Extraction Using Random Walk Image Fusion.

It is important to extract a clear background for computer vision and augmented reality. Generally, background extraction assumes the existence of a clean background shot through the input sequence, but realistically, situations may violate this assumption such as highway traffic videos. Therefore, our probabilistic model-based method formulates fusion of candidate background patches of the input sequence as a random walk problem and seeks a globally optimal solution based on their temporal and spatial relationship. Furthermore, we also design two quality measures to consider spatial and temporal coherence and contrast distinctness among pixels as background selection basis. A static background should have high temporal coherence among frames, and thus, we improve our fusion precision with a temporal contrast filter and an optical-flow-based motionless patch extractor. Experiments demonstrate that our algorithm can successfully extract artifact-free background images with low computational cost while comparing to state-of-the-art algorithms.

Camera calibration and vehicle tracking: Highway traffic video analytics

We describe a real-time highway surveillance system (RHSS), which operates autonomously to collect statistics (speed and volume) and generates incident alerts (e.g., stopped vehicles). The system is designed to optimize long-term real-time performance accuracy. It also provides convenient integration to an existing surveillance infrastructure with different levels of service. Innovations include a novel 3-D Hungarian algorithm which is utilized for object tracking and a practical, hands-off mechanism for camera calibration. Speed is estimated based on trajectories after mapping/alignment with respect to dominant paths learned based on an evolutionary dynamics model. The system, RHSS, is intensively evaluated under different scenarios such as rain, low-contrast and high-contrast lightings. Performance is presented in comparison to a current commercial product. The contribution is innovation of new technologies that enable hands-off calibration (i.e., automatic detection of vanishing points) and improved accuracy (i.e., illumination balancing, tracking via a new 3-D Hungarian algorithm, and re-initialization of background detection on-the-fly). Results indicate the capability and applicability of the proposed system in real-time and real-world settings.