1. Experiments Test different parking lot images captured in different luminance conditions The test samples include 1300 available parking spaces and 1500 occupied spaces The final false-acceptance rate (FAR) and false-rejection rate (FRR) are 0.032 and 0.02 In comparison, Dan’s method, which is a single-space detection method based on a SVM classifier, has FDR = 0.048 and FRR = 0.071 3 seconds to perform the detection and segmentation of parking spaces for a 320x240 color image on a PC with a 1.6GHz Pentium-4 CPU The world projection information is well-learned and embedded in the semantic knowledge so that the inter-object occlusion problem can be properly handled BHDF: one-row space detection  As an optimal inference problem  The statistical properties embedded in the BHDF graphical model D, S are conditional independent given H p(s i ) is assumed to be a uniform distribution “Occupied” status and “available” status are equally possible Ignore the effect of ln(p(S L )) due to uniform assumption R s (h i ) is the set of semantic nodes connecting to h i in the graph; N i is the neighbors of the ith pixel  Optimal inference Belief propagation technique BHDF: graphical learning  3-layer BHDF is a well-defined graphical model  Graphical topology is learned in advance One-to-one connections between the D layer and the H layer Four-neighboring connections inside the H layer Topological connections between the S layer and the H layer are learned based on training samples and camera calibration Note that a car can occlude only its neighbors, each semantic node only connects to its relative nodes on the H layer  Inter-layer and intra-layer message propagations are defined by using three trainable models (A,B,C)  Analyze the statuse of each row As an optimal inference problem which is subject to three aspects Local regions should be correctly classified p(D L |H L ) Adjacent regions should follow neighboring constraints p(H L ) Parked cars in the same row would match the inter- occluded patterns p(H L |S L ) One row of parking space Row-by-Row fashion  Select one row of parking space Assume each parking space as an equal-sized cube on the ground of the 3D scene Projection one row of parking space from 3D to 2D based on camera calibration BHDF: three trainable models  Local classification model p(D L |H L ) Assume each image pixel has the identical local model; We aim to calculate p(d i |h i ={G,O,C}) Non-parametric density estimation for h i ={G,O} N G and N D : normalization terms;  (.) is a symmetric kernel function d Gj / d D j denotes one of the T n / T m “ground/ otherwise” pixel samples  Overcome the luminance variation problem Train different probability models for p(d|h=G) and p(d|h=O) under different luminance conditions Select a suitable classification model dynamically Parametric density estimation for h i ={C} Converted to a new color domain: x=[u p] = T( d=[R G B] )  Combat the luminance variation problem Only the chromatic information (u,p) is used The distribution can be approximated by a Gaussian function  Adjacency model p(H L ) Accumulated histogram to approximate the probability table p(h i,h j ) based on 5000 pseudo training samples Most of the neighboring pairs follow the smoothness constraints Boundary regions, such as between “car” pixels and “ground” pixels, have quite different statistical distributions Goals  Available space number  Available space locations  Parking guidance  Monitor the changes of space status Motivations  Parking has become a problem  Intelligent parking lots surveillance systems is becoming practical  Assist users in efficiently finding empty parking spaces A BAYESIAN HIERARCHICAL DETECTION FRAMEWORK FOR PARKING SPACE DETECTION Ching-Chun Huang 1,3, Sheng-Jyh Wang 1, Yao-Jen Chang 2,3, and Tsuhan Chen 3 1 Department of Electronics Engineering, National Chiao Tung University, Taiwan. 2 Advanced Technology Center, Industrial Technology Research Institute, Taiwan 3 Department of Electrical & Computer Engineering, Carnegie Mellon University, USA. Key ideas  Background Traditional methods: P(S|D) Given one space observation (D) Decide the status of the space (S) Consider occlusion Different spaces have different occlusion patterns Occlusion is a “ pixel-level ” problem Owing to the occluded regions, adjacent spaces become highly relative Decide the status “ space by space ” is not suitable  Proposed method: Bayesian hierarchical detection framework (BHDF) to combine (D,L,S) Introduce a hidden pixel labeling layer “L” Analyze from pixel level to space level Infer the statuses of multiple spaces all together Challenges  Luminance variations  Inter-occlusions among cars  Occlusions caused by obstacles System flow Labeling results under different lighting conditions (Left) A pseudo training sample of parking lot status for global semantic model. (Right) Pre-defined “ground” regions in the black color and “otherwise” regions in the light-gray color 3D->2D occlusion Obstacle occlusion  Global semantic model p(H L |S L ) Accumulated histogram to approximate the probability table p(h i |R s (h i )) based on pseudo training samples Pseudo training samples represent the inter-occluded patterns Pseudo samples can be automatically generated based on camera projection parameters and projecting 3D-cubes to 2D