Object Detection. Part1. Presenter: Dae-Yong
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1 Object Part1 Presenter: Dae-Yong
2 Contents 1. What is an Object? 2. Traditional Object Detector 3. Deep Learning-based Object Detector
3 What is an Object? Subset of Object Recognition
4 What is an Object? Object Categorization(Classification) Object Car Pedestrian Bus Tree Bus Car Car Car Ped Object Segmentation Object Recognition Scene Categorization Urban/Intersection
5 What is an Object? Find a target object in an given image. Sequence Bus Car Car Car Ped R G x - Object Class - Object Location(x, y, width, height) B y 1. Car, (0, 250, 120, 125) 2. Car, (80, 256, 60, 40) 3. Car, (140, 245, 130, 120) 4. Pedestrian, (400, 247, 20, 70) 5. Bus, (520, 0, 110, 320) 3 Channels, 2D matrices
6 Object How can we know where objects are and what they are? - Traditional approach Feature Extraction Candidate Generation Classification - Deep Learning-based approach Deep Neural Network
7 Traditional Object
8 Traditional Object Feature Extraction Candidate Generation Classification Target Object
9 Traditional Object Feature Extraction Candidate Generation Classification Target Object
10 Traditional Object Feature Extraction Candidate Generation Classification Target Object
11 Traditional Object Size Feature Extraction Candidate Generation Classification Scale Sliding window Pyramid
12 Traditional Object Feature Extraction Candidate Generation Classification Pedestrian
13 Traditional Object Feature Extraction Candidate Generation Classification [Feature Extraction] Gradient - Color / Brightness / Gradient - Haar Feature - Scale Invariant Feature Transform(SIFT) - Local Binary Pattern(LBP) - Histogram Oriented Gradient(HOG) [Candidate Generation] - Sliding Window Search - Selective Search - Multiscale Combinatorial Grouping (MCG) - Edge-Box - Binarized Normed Gradient (BING) HOG Object Proposal
14 Traditional Object Feature Extraction Candidate Generation Classification [Classification] - AdaBoost - Random Forest - Support Vector Machine (SVM) - Latent Support Vector Machine (L-SVM)
15 Traditional Object (Demo) Target Object: REAR Vehicle Feature: LBP Classifier: Cascade Classifier
16 Deep Learning-based Object
17 Deep Learning-based Object Candidate Generation Deep Neural Network (Classification) Regions with CNN features (R-CNN), image 3 Classification (AlexNet) 2 Generate object candidates 4 Bus Car Apply image classification network to each object candidates
18 Deep Learning-based Object How convolutional neural network is worked on the image? 2 nd Feature Map 1 st Feature Map Conv. Filter
19 Deep Learning-based Object Candidate Generation Deep Neural Network (Classification) Fast Region-based Conv. Neural Net. (Fast R-CNN), April, image 3 Classification (AlexNet) Deep Conv Net. ROI Pooling Fully Conn. ROI Projection 2 Generate object candidates 4 Bus Car Computational cost is proportionally increased according to the number of candidates.
20 Deep Learning-based Object Deep Neural Network Faster Region-based Conv. Neural Net. (Faster R-CNN), June, image 3 Bus Car 2 Base model: AlexNet / VGG Region Proposal Net. ROI Pooling Fully Conn. Deep Conv Net. Feature Maps
21 Deep Learning-based Object You Only Look Once (YOLO) Single Shot MultiBox Detector (SSD)
22 Next Presentation Part2: Traditional Object from Scratch Design Basic Object Detector - Feature: HOG Features - Classification: SVM - Practice with toy example Part3: Deep Learning-based Object I? Part4: Deep Learning-based Object II?
23
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