Occlusion Patterns for Object Class Detection

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1 Occlusion Patterns for Object Class Detection Bojan Pepik1 Michael Stark1,2 Peter Gehler3 Bernt Schiele1 Max Planck Institute for Informatics, 2Stanford University, 3Max Planck Institute for Intelligent Systems 1 CVPR 2013 Presented by Carlos Arteta Reading Group: Aug 8th 2013

2 Motivation: object detection under occlusion KITTI TUD-Crossing DPM* performance drops strongly in car detection under [20-40]% occlusion when compared to [0-20]% occlusion (Pepik et al. 2013). Can explicit modelling of the occluder help DPMs? *Felzenszwalb et al. PAMI 10.

3 Contributions - A method for discovery of occlusion pattern in datasets - Occlusion models of varying complexity - Outperforms current DPMs* on hard occlusion cases *Felzenszwalb et al. PAMI 10.

4 Closely related work Tang et al. BMVC 2012 proposed a joint single and doubleperson detector. - Builds on DPM* and adds the double-person detector as additional components in the mixture of pictorial structures. - Components in the double-person detector correspond to different levels of occlusion. - Similar training as DPM* (Latent SVM). - Concluded that a joint (single and double) person detector outperforms single person detectors (Tested on TUD-Crossing) *Felzenszwalb et al. PAMI 10.

5 Dataset: KITTI* KITTI* - urban scene with 3D bounding box annotations Occlusion level in Cars *Geiger et al. CVPR12.

6 Mining occlusion patterns: pattern clustering GOAL: discover characteristic, repetitive clusters of objectobject occlusion interactions. Features: - Relative position of 2 objects (occluder left/right of occludee) - Number of objects {1,2} - Objects' orientation w.r.t the camera. - Percentage of occlusion - Truncation of occluder (is the occluder also occluded?)

7 Mining occlusion patterns: pattern clustering GOAL: discover characteristic, repetitive clusters of objectobject occlusion interactions. Average Centroid Example Clusters Some patterns are well aligned!

Object detection models: single-object model Single-object (OC-DPM): - In addition to the DPM* components, includes a component for each cluster in the occlusion pattern

8 Object detection models: single-object model Single-object (OC-DPM): - In addition to the DPM* components, includes a component for each cluster in the occlusion pattern mining. - Predicts a single bounding box around the occluded object. - As in DPMs, the energy of a configuration of parts for component c is given by *Felzenszwalb et al. PAMI 10.

9 Object detection models: double-object model Double-object symmetric (Sym-DPM): - Jointly models and detects occluder and occludee. - p0 is the tightest bounding box that includes both bounding boxes - Each component in the mixture corresponds to each cluster in the occlusion pattern mining.

10 Object detection models: double-object model Double-object asymmetric (Asym-DPM): - Similar to Sym-DPM, but omits common root part. - The connection is asymmetric in that the occluding object is trusted more as it is more visible than the occluded one.

11 Learning and NMS Learning for all models is done using a latent structural SVM formulation: Loss Function NMS Bounding box driven NMS. If predicting double objects, the bounding boxes from the same component do not suppress each other.

12 Components Learned models: OC-DPM

13 Learned models: Sym-DPM The objects (occluder and occludee) do not contain parts

14 Learned models: Asym-DPM The occludee does not contain parts

15 Experiments: detection of occlusion patterns recall recall Only occluded objects are included 1 - precision 1 - precision

16 Experiments: object detection full dataset recall recall All objects in the dataset are included. Double-object detectors are augmented with components for single-objects. 1 - precision 1 - precision The difference in AP for car class represents approximately 1000 true positives more

17 Experiments: broken into occlusion levels The performance is measured at different levels of occlusions for cars. (0-20)% Occlusion recall recall (20-40)% Occlusion 1 - precision 1 - precision

18 Experiments: broken into occlusion levels The performance is measured at different levels of occlusions for cars. (40-60)% Occlusion recall recall (60-80)% Occlusion 1 - precision 1 - precision

19 Experiments: broken into occlusion levels The performance is measured at different levels of occlusions for cars. recall (80-100)% Occlusion 1 - precision

20 DPM OC-DPM DPM OC-DPM Experiments: example results (true positives)

21 Experiments: discussion Why are the double-object detectors outperformed by the single-object detectors? (contradicts Tang et al., 2012) - A big proportion of occluded cars appear in cluster larger than 2, presenting patterns not considered by the models. - Single objects dominated over duplets due to score incompatibility. Conclusion: Occlusion patterns can be automatically mined, and modelling them explicitly can aid object detection.

22 Note about the dataset Noisy annotations (i.e. objects not annotated) in KITTI can create and underestimation of the performance of the method in this paper. Some strong false positives by the OC-DPM model

Part-Based Models for Object Class Recognition Part 3

High Level Computer Vision! Part-Based Models for Object Class Recognition Part 3 Bernt Schiele - schiele@mpi-inf.mpg.de Mario Fritz - mfritz@mpi-inf.mpg.de! http://www.d2.mpi-inf.mpg.de/cv ! State-of-the-Art