Yun-FuLiu Jing-MingGuo Che-HaoChang Low resolution pedestrian detection using light robust features and hierarchical system Yun-FuLiu Jing-MingGuo Che-HaoChang Pattern Recognition, Volume 47, Issue 4, April 2014, pp 1616–1625 Reporter: LIM ZI YI

Outline Introduction Problem descriptions Proposed probability-based pedestrian mask pre-filtering Orientation/magnitude-based AdaBoost algorithm Experimental results Conclusion Personal Remark

Introduction How to deal with the prospective complexities of background and light conditions is still a key issue in this field.

Introduction Methods with temporal information consider movement or depth information have an inherent drawback that when the camera is moving or pedestrian is not standing still, the backgroundand foreground will be misclassified. common and intuitive features, e.g., color and brightness, the performance can be easily affected by the dresses of pedestrians or environmental lighting conditions.

Introduction This study presents a hybrid pedestrian detection scheme for significantly reducing detection time. two topics are focused in this work: (1) The feature descriptors for pedestrians, and (2) the resolution of a pedestrian image.

Problem descriptions Features for pedestrian Sample resolution

Features for pedestrian Fig. 2. Conceptual diagram of the edgelet feature applied to pedestrians.

Sample resolution Another issue which directly affects driving safety is the practical detection distance. Fig. 4. Relationship between the height of pedestrian and the distance from camera.

Sample resolution Fig. 5. The difference between the same pedestrian captured with different resolutions.

Proposed probability-based pedestrian mask pre-filtering(PPMPF) Initial weight mask generation Refinement Fig. 6. Algorithm of the proposed pedestrian detection scheme.

Initial weight mask generation Two main components of the first stage: probability-based pedestrian mask the corresponding probability table (histogram table) This study uses a convolutional pedestrian mask detection strategy

Initial weight mask generation

Initial weight mask generation (4) (5)

Initial weight mask generation Fig. 7. Pedestrian pre-filtering by orientation mask.

Refinement

Refinement Fig. 8. Pedestrian pre-filtering by magnitude mask.

Orientation/magnitude-based AdaBoost algorithm Features Training procedure Detection process Fig. 9. Flowchart of the proposed training process.

Features (10) (11)

Training procedure The AdaBoost can be divided into two parts: voting. the process trains the sorted datasets to obtain the classifiers, and uses the trained classifiers to construct a strong classifier to predict the samples of interest.

Training procedure In this training procedure, the iteration updates the weights to upgrade the effect of the weak classifier to a strong classifier. As the training process is completed, a voting mechanism is applied to form a strong classifier which can yield a precise detection capability.

Training procedure Fig. 10. Orientation/magnitude-based features obtained from the average value of the blocks which are not easily affected during walking.

Detection process Fig. 11. Flowchart of the orientation/magnitude-based detection process.

Experimental results Datasets: INRIA Daimler Chrysler Pedestrian dataset (15) (16)

Experimental results Fig. 12. Precision-recall plots with different features and the corresponding derived ways (1-D and the Sobel edge detector are considered).

Experimental results Fig. 13. Precision-recall performances of the HOG, Haar-like, edgelet , and the proposed feature obtained with the INRIA dataset and the Daimler Chrysler Pedestrian dataset.

Experimental results Fig. 14. Practical detection results with various features under diverse scenarios.

Proposed entire system (with PPMPF) Experimental results Table1 Detection speed comparison. HOG Haar-like features Edgelet features Proposed feature Proposed entire system (with PPMPF) Detect windows (s) 62.35 957.78 1364.07 1339.17 3773.58

Conclusion lighting change and low- resolution scenario, are considered simultaneously for pedestrian detection application. the proposed scheme can offer an accuracy similar to that of the HOG, while the proposed scheme is much faster the processing efficiency is similar to that of the edgelet, while the propose scheme is of higher accuracy.

Personal Remark On Table 1, Author didn’t explain what the unit is. We can use other features like depth information to increase accuracy(but time of computing will become slower). If need to apply such system on driving safety system, we can use laser radar to replace this type of method.

Thank you for your listening !

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