Object Detection. Sliding Windows. Sanja Fidler CSC420: Intro to Image Understanding 1 / 49
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1 Object Detection Sliding Windows Sanja Fidler CSC420: Intro to Image Understanding 1 / 49
2 Type of Approaches Different approaches tackle detection differently. They can roughly be categorized into three main types: Find interest points, followed by Hough voting Sliding windows: slide a box around image and classify each image crop inside a box (contains object or not?) Let s look at a few methods for this Generate region (object) proposals, and classify each region Sanja Fidler CSC420: Intro to Image Understanding 2 / 49
3 Sliding Window Approaches There are many... We will look at two in more detail: Dalal and Triggs (2005): HOG (Person) Detector (9,541 citations) Felzenswalb et al. (2010): Deformable Part-based Model (2,333 citations) The last detector (DPM) is an extension of Dalal & Triggs. If we have time we ll also talk about the following approach (if not, I suggest you read it since it has some fantastic ideas): Viola and Jones (2001): (Face) Detector (10,043 citations) Sanja Fidler CSC420: Intro to Image Understanding 3 / 49
4 Sliding Window Approaches There are many... We will look at three in more detail: Dalal and Triggs (2005): HOG (Person) Detector This first Felzenswalb et al. (2010): Deformable Part-based Model Sanja Fidler CSC420: Intro to Image Understanding 4 / 49
5 The HOG Detector N. Dalal and B. Triggs Histograms of oriented gradients for human detection CVPR, 2005 Paper: Sanja Fidler CSC420: Intro to Image Understanding 5 / 49
6 The HOG Detector We want to find all people in this image. Preferably our detections should not include trees, lamp posts and umbrellas. Sanja Fidler CSC420: Intro to Image Understanding 6 / 49
7 The HOG Detector Sliding window detectors find objects in 4 very simple steps: (1.) inspect every window, (2.) extract features in window, (3.) classify & accept wind. if score above threshold, (4.) clean-up the mess (called post-processing) Sanja Fidler CSC420: Intro to Image Understanding 7 / 49
8 The HOG Detector Sliding the Window First step: inspect every window. Typically the size of window is fixed. Sanja Fidler CSC420: Intro to Image Understanding 8 / 49
9 The HOG Detector Sliding the Window Since window size is fixed, how can we find people at different sizes? Sanja Fidler CSC420: Intro to Image Understanding 9 / 49
10 The HOG Detector Sliding the Window Shrink (down-scale) the image and slide again Sanja Fidler CSC420: Intro to Image Understanding 10 / 49
11 The HOG Detector Sliding the Window Keep shrinking and sliding Sanja Fidler CSC420: Intro to Image Understanding 11 / 49
12 The HOG Detector Sliding the Window In fact, do a full image pyramid, and slide your detector at each scale. Make sure the scale differences across levels are small (do lots of re-scaled images) Sanja Fidler CSC420: Intro to Image Understanding 12 / 49
13 The HOG Detector Sliding the Window? What if the object is in a weird pose (window is of different aspect ratio)? Sanja Fidler CSC420: Intro to Image Understanding 13 / 49
14 The HOG Detector Limitations Stop thinking too hard. In 2005 people were only in upright position. We will re-visit this question a little later (when we talk about DPM) Figure: Main pedestrian detection datasets prior to PASCAL VOC. Sanja Fidler CSC420: Intro to Image Understanding 14 / 49
15 The HOG Detector Features (HOG) Famous feature descriptor called HOG that replaced SIFT (at least for object detection). There are three steps to compute it. Sanja Fidler CSC420: Intro to Image Understanding 15 / 49
16 The HOG Detector Features (HOG) First compute gradients Sanja Fidler CSC420: Intro to Image Understanding 16 / 49
17 The HOG Detector Features (HOG) There are many ways how to compute the gradients. The HOG detector guys tried a lot of them and picked the best one. Sanja Fidler CSC420: Intro to Image Understanding 17 / 49
18 The HOG Detector Features (HOG) One can also smooth image before computing the gradients. The HOG detector guys tested that as well. This is great science, analyze every step! Sanja Fidler CSC420: Intro to Image Understanding 18 / 49
19 The HOG Detector Features (HOG) Divide the image into cells of 8 8 pixels. Sanja Fidler CSC420: Intro to Image Understanding 19 / 49
20 The HOG Detector Features (HOG) Compute a histogram of orientations in each cell (similar to SIFT) Sanja Fidler CSC420: Intro to Image Understanding 20 / 49
21 The HOG Detector Features (HOG) Again, check how many bins is best to use. Turns out: 9 with orient Sanja Fidler CSC420: Intro to Image Understanding 21 / 49
22 The HOG Detector Features (HOG) So each cell now has a 9-dimensional feature vector Sanja Fidler CSC420: Intro to Image Understanding 22 / 49
23 The HOG Detector Features (HOG) In literature you will see this kind of visualization for HOG. In each cell people plot all the orientations that are present in the cell. Do not confuse this visualization with the actual feature (composed of 9 matrices). Sanja Fidler CSC420: Intro to Image Understanding 23 / 49
24 The HOG Detector Features (HOG) We re not finished. We now take blocks, where each block has 2 2 cells. Sanja Fidler CSC420: Intro to Image Understanding 24 / 49
25 The HOG Detector Features (HOG) We normalize each feature vector, such that each block has unit norm. This step doesn t change the dimension of the feature, just the strength. Why are we doing this? Sanja Fidler CSC420: Intro to Image Understanding 25 / 49
26 The HOG Detector Features (HOG) Since each cell is in 4 blocks, we have 4 different normalizations, and we make each one into separate features. Sanja Fidler CSC420: Intro to Image Understanding 26 / 49
27 The HOG Detector Features (HOG) For person class, window is 15 7 HOG cells (what s the size in pixels?) We vectorize each the feature matrix in each window. Sanja Fidler CSC420: Intro to Image Understanding 27 / 49
28 The HOG Detector Classification Features done, we are ready for classification. We first need to train our classifier, and only after we can do detection (prediction). Sanja Fidler CSC420: Intro to Image Understanding 28 / 49
29 The HOG Detector Training Several simple steps. Plus a few useful additional tricks (remember, hacking is part of the Secret Life of a Vision Researcher). Sanja Fidler CSC420: Intro to Image Understanding 29 / 49
30 The HOG Detector Training Take a dataset with annotations. If nothing exists, collect and label yourself. Sanja Fidler CSC420: Intro to Image Understanding 30 / 49
31 The HOG Detector Training Scale positive and negative examples to the size of detection window. Compute HOG. Sanja Fidler CSC420: Intro to Image Understanding 31 / 49
32 The HOG Detector Training Train a classifier (with e.g. LibSVM). Sanja Fidler CSC420: Intro to Image Understanding 32 / 49
33 The HOG Detector Training Additional tricks: Bootstrapping. A fancy name for running your classifier on training images (with full detection pipeline), and finding mis-classified windows. Add those to training examples, and re-train classifier. Sanja Fidler CSC420: Intro to Image Understanding 33 / 49
34 The HOG Detector Detection Take a window, crop out a feature matrix, vectorize and classify Sanja Fidler CSC420: Intro to Image Understanding 34 / 49
35 The HOG Detector Detection Computing the score w T x + b in every location is the same as performing cross-correlation with template w (and add b to result). [Pic from: R. Girshik] Sanja Fidler CSC420: Intro to Image Understanding 35 / 49
36 The HOG Detector Training Threshold the scores (e.g., score > 1) Sanja Fidler CSC420: Intro to Image Understanding 36 / 49
37 The HOG Detector Post-processing Perform Non-Maxima Supression (NMS) Sanja Fidler CSC420: Intro to Image Understanding 37 / 49
38 The HOG Detector Post-processing Perform Non-Maxima Supression (NMS) Sanja Fidler CSC420: Intro to Image Understanding 38 / 49
39 The HOG Detector Post-processing Perform Non-Maxima Supression (NMS) Sanja Fidler CSC420: Intro to Image Understanding 39 / 49
40 The HOG Detector Post-processing Perform Non-Maxima Supression (NMS) Sanja Fidler CSC420: Intro to Image Understanding 40 / 49
41 The HOG Detector Post-processing Done! Sanja Fidler CSC420: Intro to Image Understanding 41 / 49
42 Results Some results Sanja Fidler CSC420: Intro to Image Understanding 42 / 49
43 How Should We Evaluate Object Detection Approaches? How can we tell if our approach is doing well? What should be our evaluation? Sanja Fidler CSC420: Intro to Image Understanding 43 / 49
44 What s a Correct Detection Evaluation criteria: Detection is correct if the intersection of the bounding boxes, divided by their union, is > 50%. a 0 = area(b p B gt ) area(b p B gt ) [Source: K. Grauman, slide credit: R. Urtasun] Sanja Fidler CSC420: Intro to Image Understanding 44 / 49
45 Multiple Detections are Considered Wrong Below both detections have more than 50% overlap with ground-truth annotation. But only one will count as correct, the other(s) will count as false positive (wrong). Sanja Fidler CSC420: Intro to Image Understanding 45 / 49
46 Precision and Recall We sort all the predicted boxes (for all images) according to scores, in descending order Then for each k (location) in the list we can compute precision and recall obtained when using top k boxes in the list Sanja Fidler CSC420: Intro to Image Understanding 46 / 49
47 Precision and Recall We sort all the predicted boxes (for all images) according to scores, in descending order Then for each k (location) in the list we can compute precision and recall obtained when using top k boxes in the list Recall: Precision: recall = precision = #correct boxes #ground-truth boxes #correct boxes #all predicted boxes What s the min/max value of recall/precision? Sanja Fidler CSC420: Intro to Image Understanding 46 / 49
48 Precision and Recall We sort all the predicted boxes (for all images) according to scores, in descending order Then for each k (location) in the list we can compute precision and recall obtained when using top k boxes in the list Recall: Precision: recall = precision = #correct boxes #ground-truth boxes #correct boxes #all predicted boxes What s the min/max value of recall/precision? Sanja Fidler CSC420: Intro to Image Understanding 46 / 49
49 Precision and Recall Curve Then you can plot a precision-recall curve Which curve in the plot below is better, A or B? [Pic: Sanja Fidler CSC420: Intro to Image Understanding 47 / 49
50 Average Precision Average Precision (AP): Compute the area under the precision-recall curve What s the best AP one can get? What s the worst? AP is the standard measure for evaluating object detection performance Sometimes you may encounter notation map. This is mean Average Precision, and it s just an average of APs across different classes. [Pic from: R. Girshik] Sanja Fidler CSC420: Intro to Image Understanding 48 / 49
51 Performance of the HOG Detector (back in 2005) PR curve for the HOG detector Interesting: Look at the curve for PCA-SIFT (improved SIFT). Way down there... [Pic from: R. Girshik] Sanja Fidler CSC420: Intro to Image Understanding 49 / 49
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