Enhanced D-CNN architecture and centroid-based algorithm for real-time vehicle tracking and accident detection from surveillance videos

Air quality and reduction of emissions in the transport sector are determinant factors in achieving a sustainable global climate. The monitoring of emissions in traffic routes can help to improve route planning and to design strategies that may make the pollution levels to be reduced. In this work, a method which detects the pollution levels of transport vehicles from the images of IP cameras by means of computer vision techniques and neural networks is proposed. Specifically, for each sequence of images, a homography is calculated to correct the camera perspective and determine the real distance for each pixel. Subsequently, the trajectory of each vehicle is computed by applying convolutional neural networks for object detection and tracking algorithms. Finally, the speed in each frame and the pollution emitted by each vehicle are determined. Experimental results on several datasets available in the literature support the feasibility and scalability of the system as an emission control strategy.

As it is observed these days, the roads are crowded with different kinds of vehicles, and as a result, accidents have become more dangerous every day. In this paper, a vehicle accident detection and notification system is designed by using road surveillance camera data and machine learning techniques to use the recorded footage from the surveillance cameras to detect the accident status, activate an alerting sound, and send the notification message as a Google email (Gmail) to the specialist. The system model was made by combining two algorithms, convolutional neural networks (CNN) and support vector machines (SVM), to make a CNN-SVM hybrid model, which was then trained using two datasets. The first dataset contains 4814 images with sizes (28, 28) and extensions (jpg), and the second dataset contains 990 images with sizes (32, 32) and extensions (jpg). The outcomes scores of the evaluation matrices are: accuracy for the first dataset is 99.74% and loss is 1.14%, and for the second dataset, accuracy is 98.88% and loss is 3.39%. When testing it with real-world data, it achieved its objectives in 30 seconds, with accuracy reaching 100%.

More than 80% of video surveillance systems are used for monitoring people. Old human detection algorithms, based on background and foreground modelling, could not even deal with a group of people, to say nothing of a crowd. Recent robust and highly effective pedestrian detection algorithms are a new milestone of video surveillance systems. Based on modern approaches in deep learning, these algorithms produce very discriminative features that can be used for getting robust inference in real visual scenes. They deal with such tasks as distinguishing different persons in a group, overcome problem with sufficient enclosures of human bodies by the foreground, detect various poses of people. In our work we use a new approach which enables to combine detection and classification tasks into one challenge using convolution neural networks. As a start point we choose YOLO CNN, whose authors propose a very efficient way of combining mentioned above tasks by learning a single neural network. This approach showed competitive results with state-of-the-art models such as FAST R-CNN, significantly overcoming them in speed, which allows us to apply it in real time video surveillance and other video monitoring systems. Despite all advantages it suffers from some known drawbacks, related to the fully-connected layers that obstruct applying the CNN to images with different resolution. Also it limits the ability to distinguish small close human figures in groups which is crucial for our tasks since we work with rather low quality images which often include dense small groups of people. In this work we gradually change network architecture to overcome mentioned above problems, train it on a complex pedestrian dataset and finally get the CNN detecting small pedestrians in real scenes.

Computer vision has many applications that were implemented to the existing advance technology. Vehicle tracking and counting in video surveillance is one of them. Vehicle tracking allow the computer to detect and tracking the vehicles that capture through the video surveillance. Afterwards vehicle counting allow to the computer to count all of the vehicles on video. Background Subtraction is one of the method to perform object tracking and counting process for the system. This research implements Pixel Based Adaptive Segmenter Model for Background Subtraction method to perform object tracking and counting. This research used video capture of visual surveillance and vehicles as object to track and count. Result of this research is the system can track and count all vehicles on videos.

PurposeThe aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs.Materials and MethodsIn order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed.ResultsWhen dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects.ConclusionClinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

Road accidents have become an issue of major concern to the people. This paper presents an accident prevention mechanism developed through alcohol detection using an MQ3 alcohol sensor followed by automatic engine locking. The detection part uses an SW-420 vibration sensor to detect any sort of abnormal vibrations that may occur from a collision. This is accompanied by supervised deep learning CNN algorithms. The accident scene image is captured using a front camera built in the vehicle to be used by the deep learning model for accident prediction. Accident detection is followed by communication to the nearest emergency center using GPS and GSM modules.

In real time object tracking is an important task in various surveillance application. Nowadays, surveillance system very common in offices, collage campus, shopping malls etc. In this paper, an automated video surveillance system presented. Althrough tracking and counting system are commercially available today; there is need for further reaserach to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect situations or unusual behavior. In order to solve the problem of poor real time tracking performance using convolutional neural network. This paper proposes a fast and accurate real time video detection and tracking algorithm. Keyword: - Video surveillance system, Moving object detection, Tracking, Background subtraction. I. Introduction Real time human body detection and tracking in indoor environment deals with the concept of higher level application of image processing and computer vision. The following key components of identify to path of real time tracking are extracting a feature, background image subtraction. If two similar color objects come to close the tracker of one object can jump to associated object. The problem is called hijacking problem. Drifting problem occur when an object abruptly changes its direction to reverse. In that case it’s become very difficult to track the object because the motion model does not work. An object tracking can be understood as the problem of finding the path (i.e. Trajectory).

Convolutional neural networks (CNN) applications range from recognition and reasoning (such as handwriting recognition, facial expression recognition and video surveillance) to intelligent text applications such as semantic text analysis and natural language processing applications. Two key observations drive the design of a new architecture for CNN. First, CNN workloads exhibit a widely varying mix of three types of parallelism: parallelism within a convolution operation, intra-output parallelism where multiple input sources (features) are combined to create a single output, and inter-output parallelism where multiple, independent outputs (features) are computed simultaneously. Workloads differ significantly across different CNN applications, and across different layers of a CNN. Second, the number of processing elements in an architecture continues to scale (as per Moore's law) much faster than off-chip memory bandwidth (or pin-count) of chips. Based on these two observations, we show that for a given number of processing elements and off-chip memory bandwidth, a new CNN hardware architecture that dynamically configures the hardware on-the-fly to match the specific mix of parallelism in a given workload gives the best throughput performance. Our CNN compiler automatically translates high abstraction network specification into a parallel microprogram (a sequence of low-level VLIW instructions) that is mapped, scheduled and executed by the coprocessor. Compared to a 2.3 GHz quad-core, dual socket Intel Xeon, 1.35 GHz C870 GPU, and a 200 MHz FPGA implementation, our 120 MHz dynamically configurable architecture is 4x to 8x faster. This is the first CNN architecture to achieve real-time video stream processing (25 to 30 frames per second) on a wide range of object detection and recognition tasks.

This paper proposes a new algorithm for abnormal behavior detection in surveillance video. Firstly, the motion information image of each frame is constructed by calculating the optical flow size and the angle difference between the optical flow vectors between consecutive frames, and then the obtained motion image information is input into the convolutional neural network (CNN) for training, and used for video abnormal behavior detection. In the algorithm, the motion information image generated based on optical flow information can provide the motion information features in the video image more accurately, which makes it easier to distinguish the normal behavior and abnormal behavior of the video. The experiment of this algorithm is carried out on the commonly used data set PETS 2009. Experimental results show that the proposed method and other algorithms have a significant improvement in the accuracy of abnormal behavior detection.

The detection of poultry egg microcracks based on electrical characteristic models is a new and effective method. However, due to the disorder, mutation, nonlinear, time discontinuity, and other factors of the current data, detection algorithms such as support-vector machines (SVM) and random forest (RF) under traditional statistical characteristics cannot identify subtle defects. The detection system voltage is set to 1500 V in the existing method, and higher voltages may cause damage to the hatched eggs; therefore, how to reduce the voltage is also a focus of research. In this paper, to address the problem of the low signal-to-noise ratio of microcracks in current signals, a wavelet scattering transform capable of extracting translation-invariant and small deformation-stable features is proposed to extract multi-scale high-frequency feature vectors. In view of the time series and low feature scale of current signals, various convolutional networks, such as a one-dimensional convolutional neural network (1DCNN), long short-term memory (LSTM), bi-directional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU) are adopted. The detection algorithm of the wavelet scattering convolutional network is implemented for electrical sensing signals. The experimental results show that compared with previous works, the accuracy, precision, recall, F1-score, and Matthews correlation coefficient of the proposed wavelet scattering convolutional network on microcrack datasets smaller than 3 μm at a voltage of 1000 V are 99.4393%, 99.2523%, 99.6226%, 99.4357%, and 98.8819%, respectively, with an average increase of 2.0561%. In addition, the promotability and validity of the proposed detection algorithm were verified on a class-imbalanced dataset and a duck egg dataset. Based on the good results of the above experiments, further experiments were conducted with different voltages. The new feature extraction and detection method reduces the sensing voltage from 1500 V to 500 V, which allows for achieving higher detection accuracy with a lower signal-to-noise ratio, significantly reducing the risk of high voltage damage to hatching eggs and meeting the requirements for crack detection.

Surveillance cameras also known as Closed Circuit Tele-Vision (CCTV) play a major part in the enforcement of the law and the administration of justice. The present level of industrial production technology cannot differentiate between a good and a bad, or even a bit worse, situation. As a consequence, additional crime scene investigation or law order maintenance work is needed, which takes a lengthy time. The suggested work is being utilized for a number of purposes, including surveillance, weapon monitoring and classification, live tracking, and more. Video input is permitted as a type of raw input in this project for monitoring and detecting abnormal events utilizing realtime detection techniques such as You Look Only Once Version 3 (YOLO V3). The proposed project's operations make use of a processing module for object recognition using convolutional neural networks such as YOLO V3, which predicts classes and bounding boxes for the entire image in a single run of the algorithm. The circular area will be watched by CCTV, which will automatically execute all operations and be controlled. Before implementing in such a setting and delivering optimal results, numerous samples and datasets will be examined to find accuracy in detection and classification. The planned effort aims to significantly reduce crime rates while simultaneously providing improved protection in specific areas and decreasing the time it takes to capture a criminal.

Human action detection and recognition has become a significant topic in computer vision research over the last two decades. Intelligent techniques (such as machine learning and deep learning) have grown in popularity as a result of technological advancements in visual data. Due to the exponential growth of video data generated by surveillance cameras, intelligent systems are in great demand for detecting specific human activities. In this work, we critically examine the state-of-the-art (SOA) methodologies for human activity detection (HAD) approaches. The study illustrates a generic classification for HAD methods anda paradigm shift is observed from traditional to modern deep learning-based techniques. We also provide an analysis of publicly available datasets for the classification of human activities. The paper outlines various open research issues as well as future directions for HAD methods. Due to problems such as a dynamic and complex background, camera motion, occlusion, and bad weather, it is evident that detecting human behaviors in surveillance videos is a challenging task. Convolution neural networks (CNNs) are used in the bulk of HAD techniques, prompting researchers to look at sequence learning models like Recurrent Neural Networks (RNNs) and Long-Short-Term Neural Networks (LSTM). Furthermore, our analysis indicates that only a few research articles on the detection of anomalous behavior have been published, with the majority of the work focusing on human action detection. Furthermore, existing HAD deep learning models could be improved by including the notion of transfer learning, which saves training time and enhances accuracy.

Convolutional neural networks (CNN) is a widely known deep learning architecture and achieves higher accuracies in speech processing and computer vision applications. CNN has arisen as best option in the fields such as video surveillance, mobile robot vision, smart factories, and medical diagnostics. CNN has shown its tremendous potential for image understanding in Cardio-Vascular (CV) systems. But, the tremendous potential of CNNs came up with additional computational burden. To get an outstanding performance of CNN-based algorithms, it requires large amount of enormous applications and memory resources. Matrix multiplication (MM) is the most fundamental computational operation taking place in CNN. Researchers in this field found that multiplication operation is the most resource-intensive and power-hungry operation in CNN. This work aims to design an FPGA-based CNN using systolic array which can improve both the accuracy and hardware efficiency of convolutional neural network. Here, we made design optimisation in multiplier unit which brings a significant impact on overall performance of convolutional neural networks.

Sequential attention mechanism for weakly supervised video anomaly detection

In this fast-moving world, accidents in four wheeled vehicles occur due to the break failure or because of the carelessness or the fatigue of the driver. The driver pattern of the driver plays a major role in providing road safety as well as in fuel consumption. The distraction of drivers is found by installing various sensors which is used for gathering real time data. The behaviour of drivers under stress condition and their behavioural patterns for early detection and avoidance of accidents are found using convolutional neural networks. Convolutional Neural Networks are efficient classifiers in handling image processing and computer vision problem. The input dataset is a collection of driving behaviour of 10 different drivers collected from Kaggle. The behaviour of drivers under 7 distracted situations like texting, talking through phone, playing music, drinking, eating, doing make up and talking to passenger are considered. The batch normalization is used at the right of the input layer in order to avoid skewing of data at a direction. It is shown, the convolutional neural networks at 4 epochs have shown 99% accuracy.