A Multi-Layer Discriminative Framework For Parking Space Detection
Ching-Chun Huang, Hoang Tran Vu

In this paper, we proposed a new multi-layer discriminative framework for vacant parking space detection. From bottom to top, the framework consists of an image feature extraction layer, a patch classification layer, a weighted combination layer, and a status inference layer. In the feature extraction layer, the framework extracts lighting-invariant features to relieve the effects from lighting and shadow. In the patch classification layer, image patches are selected. In order To overcome perspective distortion, each patch was normalized. For different patch, we trained classifiers to recognize the occlusion patterns, which are treated as the middle-level feature of the parking status. In the weighted combination layer, three spaces are grouped as a unit to easily handle inter-object occlusion. Based on the middle-level features, a boosted space classifier was trained to determine the local status of a 3-space unit. In the status inference layer, we regarded these local status decisions as high-level evidences and inferred the final status of the parking lot. The results in an outdoor parking lot show our system can well handle inter-object occlusion and achieve robust vacant space detection under many environmental variations. A real-time system was also implemented to demonstrate its computing efficiency.