Pedestrian and cyclist detection system

Identifies and locates pedestrians and cyclists in images or video streams. Object detection is a subfield of computer vision that focuses on recognizing and localizing objects within an image or a video frame.

Use Case
Object Detection

The automobile industry is confronted with the crucial challenge of ensuring the safety of pedestrians and cyclists, particularly in urban environments. Traditional vehicle safety systems often fall short in accurately detecting these vulnerable road users under diverse conditions like poor lighting or inclement weather. The unpredictable movements of pedestrians and cyclists, combined with the complex dynamics of urban traffic, significantly increase the risk of accidents. There is a clear need for an innovative technological solution to effectively address these safety concerns.


We partnered with our client to develop a solution tailored to meet the specific needs of the automobile industry. This solution leverages advanced object detection and computer vision technologies to provide real-time, accurate detection and tracking of pedestrians and cyclists. It is designed not only as a technological tool but also as an integration of user-centric design and practical vehicular safety requirements. Key features of the solution include:

  1. Advanced detection capabilities using AI and machine learning, enabling the system to identify pedestrians and cyclists in challenging conditions such as low light or dense traffic.
  2. Real-time alerts and integration with automatic braking systems, giving drivers crucial time to react and prevent potential accidents.
  3. A user-friendly interface that allows drivers to adjust settings according to their driving style and conditions, enhancing the overall usability of the system.
  4. Adaptive algorithms that learn from diverse environments and traffic scenarios, continuously improving the system's accuracy and reliability.


The implementation of this solution in the automobile industry has led to significant improvements in several key areas:

  1. A marked reduction in accidents involving pedestrians and cyclists, thanks to improved detection accuracy and timely driver alerts.
  2. Enhanced driver awareness and decision-making capabilities, contributing to safer driving practices.
  3. Seamless integration with existing vehicle safety systems, resulting in a more comprehensive and effective safety strategy.
  4. Increased driver confidence and comfort, fostering a safer and more responsible driving environment.

Techstacks Used

Technologies and Tools
• ML: TensorFlow, PyTorch, scikit-learn, AutoML • Computer Vision: OpenCV, YOLO, TensorFlow Object Detection API • Integration APIs: RESTful APIs, WebSocket, MQTT • Data Analytics: Tableau, Power BI, Apache Spark • Testing and Simulation: Selenium, JUnit, Unity Test Tools, ANSYS Simulation Software

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