CAP 6412 Advanced Computer Vision

Transcription

1 CAP 6412 Advanced Computer Vision Boqing Gong April 21st, 2016

2 Today Administrivia Free parameters in an approach, model, or algorithm? Egocentric videos by Aisha

3 Project II due: next Wednesday (04/27, 5PM) Final Project Presentation: 04/28, 1 3:50 PM Late submissions: PrQTo9xTWKfv7s-OPyuV_zZw9Fc/edit?usp=sharing

4 Today Administrivia Free parameters in an approach, model, or algorithm? Egocentric videos by Aisha

5 Free parameters (hyper-parameters) In Project 2, when you train the CNNs Learning rate, momentum, weight decay, dropout rate, early stopping, etc. Network architecture, nonlinear functions, strides, etc. In Linear regression In SVM min w MX (y m x T mw)+ kwk 2 2 m=1 min w, m,m=1,,m MX m=1 m + kwk 2 2 s.t. y m (x T mw) 1 m, & m 0 8m

6 Free parameters (hyper-parameters) In K-means clustering: K, the number of clusters In K-Nearest neighbors classifier: K, the number of neighbors In Canny edge detection Gaussian filter, thresholds In R-CNN Threshold of selective search # Layers, filter size, stride, where max pooling Padding or not, learning rate, momentum, weight decay, #iterations Trade-off parameter Feature selection for regression Batch size

7 Free parameters (hyper-parameters) Free parameters vs. Model parameters min w MX (y m x T mw)+ kwk 2 2 m=1 Often seek model parameters by optimization Gradient descent (GD), coordinate descent, Newton, stochastic GD, etc. How to choose the free parameters?

8 How to choose the free parameters Smallest error rate on Test set? Validation set? Smallest expected error rate on the entire population In practice, however, we have access to a finite set of examples! Approximate the expected error rate Choose free parameters which minimize the approximate error How to approximate the expected error?

9 Weak approximation of the expected error! Rarely used in practice.

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11 Popular for small data.

12 Popular for small data.

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15 1. Divide data to training, validation, and test sets. 2. Select free parameters 1. E.g., network layers, #hidden states, nonlinear functions, etc. 3. Train the model using the training set 4. Evaluate the model using the validation set 5. Repeat steps 2 4 using different free parameters à different models 6. Select the best model (and their associated free parameters) 7. Train the model (with the associated free parameters) using both training and validation sets. 8. Assess this final model using the test set. Popular for big data. Skip step 7 for big data.

16 Skip this step for big data.

17 Today Administrivia Free parameters in an approach, model, or algorithm? Egocentric videos by Aisha

18 Hand detection in Egocentric videos Aisha Urooj Course Instructor: Dr. Boqing Gong Advanced Computer Vision

19 Motivation Emergence of new wearable technologies Action cameras Smart glasses, so on These devices capture videos from first person s perspective. Record user s experiences Image Source: [1]

20 An overview of First Person Vision

21 Image Credits: [1]

22 A hierarchical structure, starting from the raw video sequence (bottom) to the desired objectives (top) Image Credits: [1]

23 Image Credits: [1]

24 Image Credits: [1]

25 Image Credits: [1]

26 Related Datasets [1]

27 Motivation Hands are very common in egocentric videos Appearance of hands and pose give important cues about human s actions attention Activity recognition user machine interaction, so on. Most of the egocentric computer vision problems, from object detection to activity recognition requires accurate hand detection.

28 Challenges in hand detection Hands are highly deformable objects. Occlusion Cluttered background Dynamic background Inconsistent lighting Poor imaging conditions Highly dynamic camera motion So on..

29 Lending a Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions Sven Bambach, Stefan Lee, David J. Crandall, Chen Yu Indiana University

30 Outline Paper s contribution Dataset details Approach Results Possible future directions

31 Paper s Contributions Deep model for hand detection and classification in egocentric video, including fast domainspecific region proposals. A new technique for pixel wise hand segmentation. A quantitative analysis of how hand location and pose can be useful in accurate activity recognition. A large dataset of egocentric interactions with fine grained ground truth.

32 Overview Image source:

33 Ground truth hand segmentation masks on sample frames from dataset.

34 A random subset of cropped hands according to ground Truth.

35 Dataset details 4 participants, 4 activities, 3 different locations (office, home, courtyard) Total 48 unique videos. Used Google Glass, 720x1280 at 30 fps. 2 persons in one video, each wearing google glass. (Synchronized video pairs and cut them to 90 seconds) Pixel level ground truth for over hand instances. Manual annotation of 100 frames/ video i.e frames ground truth. Main Split: 36 training, 4 validation, 8 test videos.

36 Hand Detection: Approach Candidate windows generation Window classification using CNNs

37 Window Proposals Generation Probability that an object O appears in a region R of an image I. The proposed approach for candidate windows generation combines spatial biases and appearance models together.

38 Window Proposals Generation (Contd..) P (O) : Object occurrence probability P(R O) : Probability that a certain region R (a bounding box) contains a specific hand (O) P(I R, O): A pixel-level skin classifier Estimates the probability that central pixel of R is skin.

39 Coverage Results for Different Proposal Methods

40 Window classification A standard CNN classification framework used. CaffeNet from Caffe software package Slight variation of AlexNet Each training batch contains equal number of samples from each class. Disabled horizontal and vertical flipping of sample images in Caffe For differentiating between left and right hands.

41 Window classification (Contd..) The CNN weights are initialized from CaffeNet Except final fully connected layer which is set to zero mean gaussian. Fine-tuning using SGD Learning rate = Momentum = Input Generate Spatially sampled window proposals Classify window crops Using fine-tuned CNN Perform non-maximum suppression for each test frame

42 Hand Detection Two cases: Detect hands of any type Detect hand of specific type (own left, your right etc.) PASCAL VOC criteria for scoring detections is used Intersection over Union between the ground truth and detected bounding box should be > 0.5

43 Precision-Recall curves for Hand detection

44 Qualitative Results for Hand Detection

45 Quantitative Results for Hand Detection

46 Hands Segmentation Pixelwise hand segmentation is useful for: Hand pose recognition In-hand object detection, so on.. Goal: Label each pixel either to the background or to a specific hand class. Applied a semi-supervised segmentation algorithm GrabCut. Given an approximate foreground mask, GrabCut iteratively refines foreground and background pixels, relabeling them using Markov Random Field.

47 Hands Segmentation For each hand detected bounding box, initial foreground estimation is computed using same color skin model. Thresholded and marked each pixel within the box as foreground except with very low skin probability. Run GrabCut algorithm on bounding box including padded region. Final segmentation is the union of the output masks for all detected bounding boxes.

48 Quantitative Results for Hand Segmentation

49 Two modes of possible failures Failure to properly detect hand bounding boxes. Inaccuracy in distinguishing hand pixels from background. Applying segmentation algorithm on ground truth bounding boxes results in raise to average 0.73 Taking output of hand detector but using ground truth segmentation masks again increases average to 0.76

50 Qualitative Results for Hand Segmentation

51 Hand-based Activity Recognition Masked out all other non-hand background information by using ground truth hand segmentations. Fine-tuned a CNN to classify whole frames as one of the four activities. Training: 900 frames per activity for 36 videos Validation: 100 frames per activity for four videos Classification accuracy: 66.4% per frame

52 Hand-based Activity Recognition (contd..) Incorporating temporal constraints: Simple voting based approach Classify each individual frame in the context of a fixed-size temporal window centered on the frame Scores are summed across the window Frame is labeled as the highest scoring class

53 Hand-based Activity Recognition

54 Some sample hand poses not present in their dataset

55 Related work on Egocentric Hands Detection Work by A. Betancourt, University of Genoa, Italy 1)Hand Segmentation and tracking in FPV 2) A Sequential Classifier for Hand Detection in the Framework of Egocentric Vision. CVPR ) The Evolution of First Person Vision Methods: A Survey. Observations: Misses detection of hands in many frames for other people. Results show false positives in many frames. No detection on hands shown in videos running within a video. Segmentation is not efficient. At times both hands are detected as either left or right. Full arm is being considered as hand.

56 Possible Future Directions Improve segmentation technique Have an unbiased dataset Use an efficient tracking approach to incorporate temporal information Improve hand classifier

57 References [1] The Evolution of First Person Vision Methods:A Survey. A. Betancourt, P. Morerio, C. S. Regazzoni, and M. Rauterberg. IEEE Transactions on Circuits and Systems for Video Technology. Vol 25. Issue 5.

58 THANK YOU!