Figure 6 - uploaded by Abu Salman Shaikat
Content may be subject to copyright.
Source publication
Traffic jam is one of the greatest problem of Bangladesh. It affects mostly on its capital city, Dhaka, where density of population is second highest among the world. One of the major reason for occurring traffic jam is inaccuracy of the use of traffic signal. This paper introduces an intelligent traffic control system for four nodes traffic system...
Context in source publication
Citations
... The methodology of this system depends on picture handling and computerized reasoning techniques. Besides, if there should arise an occurrence of crisis, a manual framework is proposed, which can help traffic police to turn the framework to manual and control the circumstance physically (8). Here a human-insider savvy equal learning structure has been portrayed and its usage in a start to finish supported framework that impersonates and empowers proficient sign control architects' attributes. ...
Traffic congestion and regulating traffic in traffic signals are major issues in cities. Nowadays, in most of the cities, traffic management centers installed numerous cameras all over the roads and traffic signals. Such cameras can be effectively used for the automation of traffic signals. The objective is to develop a real time system that can automatically monitor real time traffic and make the system intelligent using artificial intelligence techniques. Specifically, Deep Convolutional Neural Networks are employed to perform the task. From statistical traffic data, it determines count, type of vehicle, average speed, distance between vehicles, etc. Based on traffic, the algorithm instructs to stop vehicle or queue or move. It can also record a wrong-way driver. Using license plate recognition, security applications such as unauthorized vehicles are identified. If there is violation of traffic rules, they are recorded with registration number. It can detect ambulances and give first preference. The proposed algorithm identifies VIP vehicles and clear traffics in automated ways. Ambulances are given priority to pass the road. The entire system have been developed using a standalone-Graphical User Interface (GUI). We have implemented successfully and the proposed framework performs satisfactorily.
Human falling may cause injuries and sometimes may lead to deadly conditions. Therefore, in recent decade, the systems used for monitoring of human falling and non-falling are receiving attention among research community for its diversified features and social benefits. These systems solve the problem of falling and gets activated to avert the likely incident with an alarm message, and uses fall recognition classifiers. System helps to identify the human in the intended regions, and classifiers are trained using the information available in the images. The lack of massive scale datasets and human errors limits the generalization of models in terms of robustness and efficiency to invisible regions. In the proposed work, an automatic fall detection using deep learning is modeled using dataset of falling and non-falling images. The sensitive information available in the original images is kept secure and private to maintain the safety and protection by the presented work. The experiments were conducted using real-world fall datasets having both types of human images, i.e., falling and non-falling, and the results obtained clearly indicate system enhancement for falling and non-falling image recognition using convolutional neural network (CNN) algorithm and achieving higher accuracy and reduced loss with a trained dataset which finds the optimal performance from real-time environments.
In the smart city, major crossing and most part of the road will be under the CCTV surveillance system. This influenced the community to investigate a vision-based speed and line change detection system for traffic management in the city, ensuring both road safety and efficient road design. In this paper, we proposed a deep learning model for detecting vehicle type, speed and abrupt line change using the CCTV footage in real-time. The faster region-based convolutional neural network (fr-CNN) model is chosen in this scenario, which demonstrates amazing performance in object detection. The model is trained and validated using data acquired from a self-created traffic dataset from Dhaka. According to the results of the performance evaluation, the suggested fr-CNN model for moving vehicle status detection system outperforms the mobile-net single-shot multibox detection technique in terms of overall performance.KeywordsObject detectionCNNSingle-shot multibox detectionRoad-trafficOpen CVTensor flow
