UnifiedPoseNet-A Lightweight Shared Pose Estimation Model for Human and Vehicles

Abstract

Pose estimation is a key aspect of action recognition, study of behaviors and spatial relationships from video data. Traditional pose estimation methods are designed exclusively for keypoint detection of either humans or vehicles and incur high inference time and complexity. In this paper, we present UnifiedPoseNet, a lightweight convolutional network that performs 2D keypoint estimation simultaneously for both humans and vehicles in a single forward pass without depending on a bounding box. This joint approach addresses challenges in smart city applications like surveillance and traffic monitoring- where pedestrians and cars interact. The UnifiedPoseNet is built on a novel HVR-Net backbone that learns spatiotemporal features for both entities jointly, coupled with a Differentiable Feature Recombination Block (DFRB) to exchange contextual cues between human and vehicle representations. Dual decoders generate heatmap confidence maps for 17 human keypoints and 6 vehicle keypoints, enabling pose detection in mixed-scene environments. Our model employs a novel hybrid multi-task loss function combining focal and entity-aware terms: each training image carries an entity presence mask to enable focus only on relevant keypoints, thus making learning robust to missing annotations. Our model achieves a keypoint accuracy of 88.4% for humans and 85.9% for vehicles, with an FPS of 23 and a model size of 4.1MB demonstrating real-time edge efficiency. Comparative results against lightweight state-of-the-art baselines for human and vehicle pose estimation—demonstrate that UnifiedPoseNet attains comparable accuracy to specialized models while requiring significantly fewer parameters and offering a unified framework for simultaneous human–vehicle keypoint estimation.