Edge AI Surveillance: 19-Stream Real-Time Detection

The Challenge

A surveillance solutions provider needed to process 19 RTSP video streams simultaneously at 2K resolution with real-time object detection and tracking. The system had to run entirely on-premises — no cloud dependency — to reduce latency and operational costs while maintaining privacy compliance.

Our Approach

We engineered a high-throughput edge inference pipeline optimized for maximum stream density on a single NVIDIA Jetson device.

Video Pipeline

GStreamer handles RTSP stream ingestion, decoding, and batching. The NVIDIA DeepStream SDK orchestrates the end-to-end pipeline from video decode through inference to metadata output, leveraging hardware-accelerated video processing.

Detection & Tracking

YOLOv8 performs object detection, with models quantized to INT8 precision via TensorRT for maximum throughput. NvDCF (NVIDIA Discriminative Correlation Filter) provides persistent object tracking across frames with minimal computational overhead.

Custom Processing

A custom GStreamer video processing filter handles application-specific logic — zone intrusion detection, dwell time analysis, and alert generation — running inline with the inference pipeline.

Technical Stack

• Hardware: NVIDIA Jetson AGX Xavier
• Pipeline: DeepStream SDK, GStreamer
• Detection: YOLOv8 (INT8 quantized via TensorRT)
• Tracking: NvDCF
• Formats: ONNX, PyTorch
• Languages: C++, Python

Results

• 19 simultaneous video streams at 2K resolution
• 8-10 FPS real-time inference per stream
• Zero cloud dependency — fully edge-based processing
• Significant reduction in operational costs vs. cloud solutions
• Scalable architecture for additional analytics modules