Advanced Car and Pedestrian Tracker Using Machine Learning Techniques for Enhanced Traffic Monitoring and Safety

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Project Abstract:

Background: Traffic monitoring and pedestrian safety are critical aspects of modern urban transportation systems. With the increasing availability of video surveillance data, there is an opportunity to develop advanced machine learning (ML) models for tracking cars and pedestrians in real-time. This project aims to create a robust and efficient car and pedestrian tracker using machine learning techniques, which will facilitate enhanced traffic monitoring, improved road safety, and the potential for integrating with smart city infrastructure.


  1. To acquire, preprocess, and annotate video data of traffic scenes containing cars and pedestrians from various sources.
  2. To implement various feature extraction techniques to obtain relevant features for tracking cars and pedestrians in the video data.
  3. To explore a range of machine learning algorithms, such as object detection, tracking, and deep learning methods, to create a high-performance car and pedestrian tracking model.
  4. To evaluate the performance of the tracking model using appropriate metrics and validate its effectiveness in real-world traffic monitoring scenarios.
  5. To develop a user-friendly interface for the integration of the developed model into traffic monitoring systems and smart city infrastructure.


  1. Data acquisition and preprocessing: The project will involve the collection of video data containing cars and pedestrians from multiple sources, including public databases and real-time surveillance feeds. Data preprocessing steps, such as video frame extraction, annotation, and data augmentation, will be performed to prepare the data for ML model training.
  2. Feature extraction: Techniques such as Histogram of Oriented Gradients (HOG), Scale-Invariant Feature Transform (SIFT), and deep learning-based approaches, such as Convolutional Neural Networks (CNN), will be employed to extract relevant features for car and pedestrian tracking.
  3. Model development: ML algorithms, including Faster R-CNN, YOLO, SSD, DeepSORT, and other state-of-the-art object detection and tracking models, will be applied to develop the car and pedestrian tracking model. Hyperparameter tuning and model selection will be conducted through cross-validation and grid search techniques.
  4. Model evaluation: The performance of the ML models will be assessed using metrics such as accuracy, Intersection over Union (IoU), and Multiple Object Tracking Accuracy (MOTA).
  5. Interface development: A user-friendly interface will be designed to allow easy integration of the developed model into existing traffic monitoring systems and future smart city infrastructure, promoting widespread adoption and enhanced safety.

Expected Outcomes: The project will result in a highly accurate car and pedestrian tracking model capable of processing video data in real-time for enhanced traffic monitoring and pedestrian safety. The implementation of this model in urban transportation systems will contribute to improved road safety, efficient traffic management, and the potential for integration with smart city infrastructure.

Keywords: Car and pedestrian tracking, machine learning, video surveillance, feature extraction, data preprocessing, model evaluation, traffic monitoring, smart city integration.

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