Project Experience: AI-Based Human Detection System
Project Title: Real-Time Human Detection Using AI and Computer Vision
Objective:
Developed and deployed a robust AI-powered human detection system for real-time applications such as surveillance, crowd analysis, and pedestrian detection, ensuring high accuracy and low latency in diverse environmental conditions.
Key Responsibilities:
Requirement Analysis and Scope Definition
Collaborated with stakeholders to define project goals, use cases, and performance metrics.
Identified edge cases, including occlusions, variable lighting, and crowded environments.
Dataset Development and Preprocessing
Acquired and curated a diverse dataset with real-world human images under varied conditions.
Applied advanced preprocessing techniques, such as data augmentation, normalization, and annotation of bounding boxes.
Model Development
Leveraged state-of-the-art architectures like YOLOv5 and Faster R-CNN for object detection.
Fine-tuned pre-trained models using transfer learning to accelerate development and improve performance.
Training and Validation
Conducted model training using frameworks like TensorFlow and PyTorch.
Optimized hyperparameters (learning rate, batch size, etc.) to achieve high precision and recall.
Evaluated model performance with metrics such as mAP (mean Average Precision) and F1 score.
Optimization and Deployment
Optimized the model for edge devices using TensorRT and OpenVINO for faster inference.
Integrated the system into existing applications with APIs and real-time data pipelines.
Testing and Performance Tuning
Conducted rigorous testing in diverse environments to ensure robustness and reliability.
Resolved issues related to false positives/negatives and enhanced system accuracy.
Post-Deployment Monitoring and Maintenance
Deployed the solution on cloud and edge platforms, ensuring scalability and low latency.
Implemented a feedback loop for continuous monitoring and active learning, updating the model periodically to improve accuracy.
Tools & Technologies:
Programming Languages: Python, C++
Frameworks: TensorFlow, PyTorch, OpenCV
Optimization Tools: TensorRT, OpenVINO, ONNX
Deployment Platforms: AWS, NVIDIA Jetson, Raspberry Pi
Annotation Tools: LabelImg, CVAT
Achievements:
Reduced inference time by 30% using model optimization techniques.
Achieved a detection accuracy of over 95% across diverse environmental conditions.
Deployed the solution to monitor and analyze live video feeds, supporting real-time decision-making for clients.
Impact:
The project improved operational efficiency and safety in real-world applications, including automated surveillance, traffic monitoring, and crowd management systems. It demonstrated a significant reduction in human monitoring errors and provided actionable insights through accurate and timely detections.