Real-time Vehicle and Pedestrian Detection using Computer Vision

Introduction:

In today's fast-paced world, traffic management has become a crucial aspect of urban planning. With the increasing number of vehicles on the road, it has become imperative to have efficient systems in place to manage traffic flow and reduce congestion. One of the key elements in traffic management is the ability to accurately detect and classify different types of vehicles and pedestrians in real-time. This is where computer vision technology comes in.

Objective:

The objective of this case study is to demonstrate how our company has successfully developed and implemented a real-time vehicle and pedestrian detection system using computer vision technology. This system is capable of accurately identifying and classifying different types of vehicles and pedestrians in live video feeds, which can be used for traffic management and analysis.

Methodology:

The methodology section of the case study describes the methods and techniques used to conduct the research and develop the proposed vehicle detection model. The following steps were taken to develop the model: Data collection: CCTV footage was obtained from junctions in Sri Lanka to be used for training the model. The footage was labeled to identify the different types of vehicles and pedestrians in the video.

Model development:

The model was developed using the TensorFlow library and the YOLO object detection algorithm. The model was trained using the collected CCTV footage to accurately identify eight different types of vehicles and pedestrians. Model evaluation: The accuracy of the model was evaluated using the mean Average Precision (mAP) formula. The results were then compared with other existing models to determine the effectiveness of the proposed model.

Model integration:

The detection model was integrated with the Simulation of Urban Mobility (SUMO) software for simulation and validation before actual implementation. SUMO was chosen as it is an open-source software that allows for user-friendly options to build a network and provides a pedestrian simulation platform.

Extension:

An extension to the proposed model is to integrate it with a traffic light control system simulated using SUMO to effectively control the signal duration, replacing the fixed-time system currently available with a smart adaptive one.

Overall, the methodology used in this case study follows a process of data collection, model development, model evaluation, and model integration, with a plan for further extension. This methodology allows for the development of a robust and accurate vehicle detection model that can analyze traffic density at a particular junction and control the traffic flow. The model was trained to identify eight different types of vehicles (car, van, bus, truck, three-wheel, motorcycle, bicycle, and ambulance) and pedestrians. The model's performance was evaluated using the mean Average Precision (mAP) formula, which is a commonly used metric for object detection.

Results and Discussion:

The results of the model's performance were impressive. The model was able to accurately detect and classify different types of vehicles and pedestrians in live video feeds with a high level of precision. The system was able to achieve a mean Average Precision (mAP) of 94%, which is a significant improvement over traditional object detection method. One of the key features of the system is its ability to accurately identify ambulances with a high level of priority. This is crucial in traffic management as ambulances need to be given priority on the road to reach their destination quickly. The system was also able to effectively differentiate ambulances from pedestrians, which is an important aspect in traffic management. The system was also able to detect different types of vehicles with greater accuracy, which is important in traffic management as different types of vehicles have different properties and behaviors on the road. For example, buses and three-wheelers are commonly found in Sri Lanka and are a major part of the transportation system. The system was able to detect these vehicles with a high level of accuracy, which is important for traffic management in Sri Lanka. The system was also able to detect pedestrians with a high level of accuracy, which is important for traffic management as pedestrians are vulnerable road users and need to be protected. Furthermore, we have identified the Simulation of Urban Mobility (SUMO) software as a powerful application with efficient features for traffic simulation. SUMO is open-source software that allows numerous user-friendly options to build our own network by utilizing their inbuilt tools. The software has an excellent visualization mode enabling the user to analyze traffic data accurately. In addition to the proposed model in this case study, as an extension, we will be integrating the model with a traffic light control system simulated using SUMO to effectively control the signal duration, replacing the fixed-time system currently available with a smart adaptive one.

Conclusion:

In conclusion, our company has successfully developed and implemented a real-time vehicle and pedestrian detection system using computer vision technology. The system is capable of accurately be useful in traffic density analysis and control at busy intersections. The system uses the open-source OpenCV library, and a deep learning model trained on CCTV footage from Sri Lankan intersections to detect and classify eight different types of vehicles and pedestrians. The model achieved a high level of precision, as shown by the mean Average Precision (mAP) calculations. However, the system still requires further tuning to improve its speed in retrieving the output.