Name
Abstract | ||
|---|---|---|
Handling occlusions in object detection is a long-standing problem. This paper addresses the problem of X-to-X-occlusion-free object detection (e.g. car-to-car occlusions in our experiment) by utilizing an intuitive coupling-and-decoupling strategy. In the "coupling" stage, we model the pair of occluding X's (e.g. car pairs) directly to account for the statistically strong co-occurrence (i.e. coupling). Then, we learn a hierarchical And-Or directed acyclic graph (AOG) model under the latent structural SVM (LSSVM) framework. The learned AOG consists of, from the top to bottom, (i) a root Or-node representing different compositions of occluding X pairs, (ii) a set of And-nodes each of which represents a specific composition of occluding X pairs, (iii) another set of And-nodes representing single X's decomposed from occluding X pairs, and (iv) a set of terminal-nodes which represent the appearance templates for the X pairs, single X's and latent parts of the single X's, respectively. The part appearance templates can also be shared among different single X's. In detection, a dynamic programming (DP) algorithm is used and as a natural consequence we decouple the two single X's from the X-to-X occluding pairs. In experiments, we test our method on roadside cars which are collected from real traffic video surveillance environment by ourselves. We compare our model with the state-of-the-art deformable part-based model (DPM) and obtain better detection performance. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-37331-2_13 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
x-to-x occluding pair,occluding x pair,x-to-x-occlusion-free object detection,state-of-the-art deformable part-based model,different single x,object detection,occlusion-free car detection,occluding x,x pair,hierarchical model,single x,detection performance | Object detection,Computer vision,Dynamic programming,Coupling,Pattern recognition,Computer science,Support vector machine,Decoupling (cosmology),Directed acyclic graph,Artificial intelligence,Template,Hierarchical database model | Conference |
Volume | Issue | ISSN |
7724 LNCS | PART 1 | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Li | 1 | 63 | 4.01 |
| Tianfu Wu | 2 | 331 | 26.72 |
| Wenze Hu | 3 | 80 | 5.58 |
| Mingtao Pei | 4 | 246 | 26.35 |