Development of Real-Time Object Detection and Tracking Web Service for Surveillance and Analytics

The client requires an efficient, flexible, and scalable system to perform real-time detection and tracking of various objects within video streams. The current infrastructure lacks the capacity to support low-latency analysis, multi-object tracking, and multi-source stream processing, limiting their ability to deliver comprehensive surveillance, traffic analysis, and automated security alerts across diverse application scenarios.

A mid-to-large scale tech firm specializing in computer vision solutions for video analytics, security, and IoT integrations, aiming to enhance surveillance and traffic management capabilities.

• Develop a scalable web-based real-time detection and tracking system supporting multiple object types (vehicles, people, animals, plants) with capacity for future expansion.
• Achieve low latency processing (~450ms for on-demand images) enabling immediate analytics and decision-making.
• Implement smooth, adaptive livestreaming capabilities with peer-to-peer technology and a maximum end-to-end latency of approximately 2 seconds.
• Enable processing of various stream inputs, including IP cameras, webcams, and video files, supporting multiple concurrent clients.
• Design a modular microservices architecture using containerization (e.g., Docker) for deployment flexibility and maintainability.
• Support multiple operational modes: single-image processing via REST API and continuous stream analysis.

• Real-time vehicle, pedestrian, animal, and plant detection using deep learning-based models.
• Multi-object tracking system to maintain consistent object identities across frames.
• Multiple operational modes including single frame detection via REST API and continuous livestream analysis.
• Websocket-based two-way communication for real-time annotations and live video streaming.
• Support for various stream sources such as IP cameras, webcams, and stored video files.
• Multi-client support allowing multiple users to view annotated streams simultaneously.
• Microservices architecture deployed via Docker containers to ensure system scalability and maintainability.

• Video stream sources such as IP cameras and web cameras
• Video file repositories or storage systems for batch processing
• Client web applications for live stream viewing and interaction

• Latency: On-demand image processing under 450ms; end-to-end livestream latency approximately 2 seconds
• Scalability: Support multiple concurrent clients and multiple stream sources
• Reliability: Robust continuous operation with minimal downtime
• Security: Secure data transmission and user authentication
• Maintainability: Modular microservices deployment for easy updates and scaling

The implementation of this real-time object detection and tracking system is expected to significantly enhance the client's surveillance and analytics capabilities. Key benefits include improved traffic management efficiency, real-time security alerts, and scalable video processing infrastructure, leading to increased operational accuracy and reduced response times across various application domains. The system aims to support low latency processing and multi-source integration, thereby enabling more comprehensive and timely insights into monitored environments.