Real-Time Video Object Detection System Development

Real-Time Object Detection on Video System Development

Real-time object detection on video is a task with strict latency requirements. Real-time threshold: for surveillance systems — 25+ FPS, for robotics — 30+ FPS with latency < 33ms. Performance depends on three factors: model architecture, hardware accelerator, and inference pipeline efficiency.

System Architecture

Camera → Frame Capture → Preprocessing → Inference → Postprocessing → Output
                ↓                              ↓
        Frame Skipping              TensorRT/ONNX Runtime
        Resize/Normalize            GPU batching

For RTSP/IP cameras, use GStreamer or FFmpeg for stream capture with hardware decoding (NVDEC on NVIDIA):

import cv2

# Hardware-accelerated RTSP capture
cap = cv2.VideoCapture(
    'rtsp://camera_ip/stream?'
    'pipeline='
    'rtspsrc location=rtsp://camera_ip/stream !'
    'rtph264depay ! h264parse ! nvh264dec !'  # NVDEC
    'videoconvert ! appsink',
    cv2.CAP_GSTREAMER
)

Model Optimization for Real-Time

TensorRT optimization provides 2–5x speedup vs PyTorch:

from ultralytics import YOLO

model = YOLO('yolov8n.pt')
# Export to TensorRT FP16
model.export(
    format='engine',
    half=True,      # FP16 precision
    batch=1,        # or batch=4 for batching
    device=0,
    workspace=4     # GB for optimization
)

YOLOv8n with TensorRT FP16 on T4: 280+ FPS at 640×640 resolution.

Frame skipping — detect not every frame. At 30 FPS video, detect every 3rd frame (10 detections/sec) + track for intermediate frames. Perceived quality is preserved.

Dynamic batching — group frames from multiple cameras into batch for single GPU pass:

class MultiCameraInference:
    def __init__(self, model_path, num_cameras=8):
        self.model = load_trt_model(model_path)
        self.batch_size = num_cameras

    def process_batch(self, frames: list[np.ndarray]) -> list[list]:
        # Preprocessing batch
        batch = preprocess_batch(frames)  # [N, 3, H, W]
        # Single GPU inference for all cameras
        results = self.model.infer(batch)
        return postprocess_batch(results)

Multi-Camera Systems

For monitoring with 8–32 cameras: one A100/H100 GPU handles up to 32 streams of 1080p@30fps with YOLOv8n. Architecture: shared inference server (Triton) + separate capture processes for each camera.

Throughput:

• NVIDIA T4 (16GB): 8–12 cameras 1080p with YOLOv8m
• NVIDIA A100: 24–32 cameras 1080p with YOLOv8l

Latency Optimization

Pipeline latency = capture + decode + preprocess + inference + postprocess + display

Deployment and Monitoring

Docker container with CUDA 12.x + TensorRT. Metrics: FPS per camera, inference latency, GPU utilization, detection count per class per minute. Alerting via Prometheus + Grafana.