Optimizing Real-Time Multi-Object Tracking (YOLOv8 + DeepSORT) on CPU
A deep dive into the engineering trade-offs required to run accurate, high-FPS object tracking on commodity hardware — no GPU required.
The assumption that cutting-edge computer vision requires expensive GPU hardware is increasingly obsolete — if you're willing to optimize. Motion-Tracker was born from the need to deploy YOLOv8 + DeepSORT tracking in environments where GPU resources are unavailable or cost-prohibitive.
The first optimization lever is model quantization. By converting YOLOv8's weights from FP32 to INT8 using ONNX Runtime, inference latency dropped by ~3× with minimal accuracy degradation on the target tracking benchmarks. The second lever is frame-skipping with state interpolation: rather than running detection on every frame, we run it every N frames and use Kalman filter predictions to maintain smooth bounding box trajectories in between.
DeepSORT's re-identification network was the heaviest remaining bottleneck. Replacing the standard ResNet-50 appearance extractor with a lightweight MobileNetV3 — fine-tuned on the same dataset — halved the per-frame cost while preserving identity consistency across long occlusion windows.
The result: stable 18–24 FPS multi-object tracking on a mid-tier laptop CPU, with identity retention across full-frame occlusions up to ~40 frames. This opens up real-world deployment for edge security systems, wildlife monitoring, and smart traffic analysis without cloud dependency.