Dataset Evaluation for Multi Vehicle Detection using Vision Based Techniques

Vehicle detection is one of the primal challenges of modern driver-assistance systems owing to the numerous factors, for instance, complicated surroundings, diverse types of vehicles with varied appearance and magnitude, low-resolution videos, fast-moving vehicles. It is utilized for multitudinous applications including traffic surveillance and collision prevention. This paper suggests a Vehicle Detection algorithm developed on Image Processing and Machine Learning. The presented algorithm is predicated on a Support Vector Machine(SVM) Classifier which employs feature vectors extracted via Histogram of Gradients(HOG) approach conducted on a semi-real time basis. A comparison study is presented stating the performance metrics of the algorithm on different datasets.

Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving objects in the image space and the minimum circumscribed rectangle (BLOB) of moving objects in the geographic space to calculate the projected size of moving objects in the image space, by which we can set an adaptive threshold for each moving object to precisely remove the noise interference during moving object detection. Further, we propose a moving object detection algorithm called GMM_BLOB (GMM denotes Gaussian mixture model) to achieve high-precision detection and noise removal of moving objects. The case-study results show the following: (1) Compared with the existing object detection algorithm, the median error (MD) of the MOD-AT algorithm is reduced by 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, indicating that the accuracy of the MOD-AT algorithm is higher in single-frame detection; (2) in terms of overall accuracy, the performance and time efficiency of the MOD-AT algorithm is improved by 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.

Novel deep learning model for vehicle and pothole detection

The most important aspect of automatic driving and traffic surveillance is vehicle detection. In addition, poor road conditions caused by potholes are the cause of traffic accidents and vehicle damage. The proposed work uses deep learning models. The proposed method can detect vehicles and potholes using images. The faster region-based convolutional neural network (CNN) and the inception network V2 model are used to implement the model. The proposed work compares the performance, accuracy numbers, detection time, and advantages and disadvantages of the faster region-based convolution neural network (Faster R-CNN) with single shot detector (SSD) and you only look once (YOLO) algorithms. The proposed method shows good progress than the existing methods such as SSD and YOLO. The measure of performance evaluation is Accuracy. The proposed method shows an improvement of 5% once compared with the previous methods such as SSD and YOLO.

COMPUTER VISION BASED TRAFFIC RULE BREACH DETECTION

This paper is about the detection of traffic rule breach via computer vision which takes the feed from the traffic surveillance system, processes the video feed, detects the breach and alerts the traffic police. The number of traffic accidents is on the rise with the increasing number of vehicles. Traffic breach is the biggest cause of accidents. So, to mitigate this problem our system processes the CCTV camera feed in real-time, detects the traffic rule breach events and sends the push notification to the android based application of the traffic police stationed nearby; so, further actions can be taken. As this system detects breach faster than humans, the concerned authoritarian department will be at ease in implementing safe roads accurately. This system acts as an add-on to the current video surveillance system rather than building new infrastructure. Thus, the output of this system can be used not only or safety and security purposes but as well as for analytical purposes with effective traffic monitoring at a lower cost. Hence, this system aids law enforcement agencies in implementing road safety efficiently and effectively ensuring smooth traffic flow.

VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACH

We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset’s different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic.

Shadow Detection and Removal Technique using CNN

The Shadow detection and removal Technique is used in many real-world applications, such as surveillance systems, computer vision applications and indoor outdoor system. The shape and orientation of an object, as well as the light source, can be revealed by shadows in an image. In a traffic surveillance system, the shadow can misclassify the actual target, lowering the system’s accuracy. Numerous algorithms and techniques have been developed by researchers to aid in the detection and removal of shadows in images. This paper aims to provide an overview of different shadow detection and removal techniques, their advantages and drawbacks. Also implementation of Convolutional Neural Network for shadow detection and OpenCV features to remove shadows by re-designing the output and analysing different loss functions to train the network. Keywords: Shadow Detection and Removal Techniques, Shadow Image Processing.