Alexandre Alahi Vignesh Ramanathan. Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-1!! 4-May-15!
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1 Project 2 Q&A Alexandre Alahi Vignesh Ramanathan Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-1!! 4-May-15!
2 TLD Review Error metrics Code Overview Outline Project 2 Report Project 2 PresentaCons Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-2!! 4-May-15!
3 TLD Review Error metrics Code Overview Outline Project 2 Report Project 2 PresentaCons Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-3!! 4-May-15!
4 TLD review Tracker & Detector (T&D) are running in parallel Both contribute Not visible is a possible output Updates of T&D depends on Learning module (L) Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-4!! 4-May-15!
5 TLD: Tracking Median- shiq tracker: EsCmate translacon & scale Tracker validacon: Detector is updated If forward- back ward consistent Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-5!! 4-May-15!
6 TLD: DetecCon Three stages: - 1 st stage filtering (patch variance) - 2 nd stage: DetecCon model - 3 nd stage classifier: NN, NCC confidence = d - /(d - +d + ) Patch variance 1 Ensemble classifier (,..., ) 2 1-NN classifier 3 Accepted patches Rejected patches Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-6!! 4-May-15!
7 Slide credit from D. Capel (h\p://vision.cse.psu.edu/seminars/talks/2009/random_`f/forestsandfernstalk.pdf) Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6 -! 4-May-15!
8 TLD: DetecCon Three stages: - 1 st stage filtering (patch variance) - 2 nd stage: DetecCon model - 3 nd stage classifier: NN, NCC confidence = d - /(d - +d + ) Patch variance 1 Ensemble classifier (,..., ) 2 1-NN classifier 3 Accepted patches Rejected patches Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-8!! 4-May-15!
9 TLD: Learning P- constraints: Patches close to trajectory update the detector with PosiCve label N- constraints: Non- maximally confident deteccons update the detector with NegaCve label Both constraints make errors. Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-9!! 4-May-15!
10 TLD: Integrator Tracker" Detector" Integrator" Found box" Found box" No box" Found box" Found box" No box" No box" No box" You need to implement the output Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6 -! 4-May-15!
11 TLD: Learning (init) For 1 st frame: Sample 200 P For other frames: Sample 100 P Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6 -! 4-May-15!
12 TLD: Learning (model update) Augment both P & N when : - the patch is wrongly classified by NN ó when integrator relies on tracker response The NN uses a threshold to determine P & N patches Integrator" NN" Retain Or discard" P" N" Retain as P" N" P" Retain as N" P" P" Discard" N" N" Discard" Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-12!! 4-May-15!
13 TLD QuesCons? Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-13!! 4-May-15!
14 TLD Review Error metrics Code Overview Outline Project 2 Report Project 2 PresentaCons Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-14!! 4-May-15!
15 DeviaCon from ground- truth Ground- truth bounding box Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-15!! 4-May-15!
16 DeviaCon from ground- truth Ground- truth bounding box Bound box from TLD (confidence) Conf=0.9 Conf=0.2 Conf=0.7 Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-16!! 4-May-15!
17 DeviaCon from ground- truth Ground- truth bounding box Bound box from TLD (confidence) Compute overlap as (IntersecCon area)/(union area) IntersecCon Conf=0.9 Conf=0.2 Conf=0.7 Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-17!! 4-May-15!
18 DeviaCon from ground- truth Ground- truth bounding box Bound box from TLD (confidence) Compute overlap as (IntersecCon area)/(union area) Union Conf=0.9 Conf=0.2 Conf=0.7 Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-18!! 4-May-15!
19 DeviaCon from ground- truth Ground- truth bounding box Bound box from TLD (confidence) Compute overlap as (IntersecCon area)/(union area) Conf=0.9 Conf=0.2 Conf=0.7 Overlap = 0.7 Overlap = 0.55 Overlap = 0.15 Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-19!! 4-May-15!
20 Metric 1: Average Overlap overlap between ground- truth and tracked bounding box in frame #i Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-20!! 4-May-15!
21 DeviaCon from ground- truth Ground- truth bounding box Bound box from TLD (confidence) Conf=0.9 Conf=0.2 Conf=0.7 Overlap = 0.7 Overlap = 0.55 Overlap = Average Overlap = Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-21!! 4-May-15!
22 Problem with average overlap Doesn t account for confidence score from tracking algorithm. More confident boxes should be weighted higher Conf=0.9 Conf=0.2 Conf=0.7 Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-22!! 4-May-15!
23 Metric 2: Mean Average Precision 1. Sort frames by confidence of bounding box from TLD algorithm Frame #1 Frame #3 Frame #2 Conf=0.9 Conf=0.2 Conf=0.7 Overlap = 0.7 Overlap = Overlap = 0.55 Decreasing confidence Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-23!! 4-May-15!
24 Metric 2: Mean Average Precision 1. Sort frames by confidence of bounding box from TLD algorithm 2. A bounding box from TLD is said to be tracked correctly if the overlap > 0.5 Frame #1 Frame #3 Frame #2 Conf=0.9 Conf=0.2 Conf=0.7 Overlap = 0.7 Overlap = Overlap = 0.55 Correct Wrong Correct Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-24!! 4-May-15!
25 Metric 2: Mean Average Precision 1. Sort frames by confidence of bounding box from TLD algorithm 2. A bounding box from TLD is said to be tracked correctly if the overlap > Compute precision at different values of recall Correct Wrong Correct Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-25!! 4-May-15!
26 Metric 2: Mean Average Precision 1. Sort frames by confidence of bounding box from TLD algorithm 2. A bounding box from TLD is said to be tracked correctly if the overlap > Compute precision at different values of recall recall = 0.33 precision = 1.0 Correct Wrong Correct Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-26!! 4-May-15!
27 Metric 2: Mean Average Precision 1. Sort frames by confidence of bounding box from TLD algorithm 2. A bounding box from TLD is said to be tracked correctly if the overlap > Compute precision at different values of recall recall = 0.67 precision = 0.67 Correct Wrong Correct Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-27!! 4-May-15!
28 Metric 2: Mean Average Precision 1. Sort frames by confidence of bounding box from TLD algorithm 2. A bounding box from TLD is said to be tracked correctly if the overlap > Compute precision at different values of recall 4. Compute average precision Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-28!! 4-May-15!
29 Metric 2: Mean Average Precision 1. Sort frames by confidence of bounding box from TLD algorithm 2. A bounding box from TLD is said to be tracked correctly if the overlap > Compute precision at different values of recall 4. Compute average precision recall = 0.33 precision = 1.0 recall = 0.67 precision = 0.67 Correct Wrong Correct Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-29!! 4-May-15!
30 TLD Review Error metrics Code Overview Outline Project 2 Report Project 2 PresentaCons Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-30!! 4-May-15!
31 What We Provide TLD_project_starter_codes_release. tar.gz Matlab wrapper with various uclity funccons and display methods for TLD tracking Also includes evaluacon code Modified from original implementagon of TLD by Zendek Kalal tiny_tracking_data.tar.gz 4 validacon video sequences (sequence of image frames) 5 test video sequences (sequence of image frames) inicalizing bounding box on first frame + ground- truth bounding box in each frame All videos less than 200 frames Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-31!! 4-May-15!
32 Starter code: Param. IniCalizaCon (A modified version of the original Matlab implementacon from Zendek Kalal) run_tld_on_video.m Sets up tld parameters, calls tldexample and saves tracking results to a text file TODO: Set all the parameters for the TLD algorithm Minimal window size of object bbox Patchsize to resize every patch before learning/ deteccon Parameters specific to your learning algo (such as regularizacon constant) Parameters for seleccng posicve and negacve patches for learning Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-32!! 4-May-15!
33 Starter code: Wrapper (A modified version of the original Matlab implementacon from Zendek Kalal) tldexample.m (Nothing to do) IniCalizes with tldinit Calls the tldprocessframe funccon on every frame Also saves the output images with tracked bbox to output directory Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-33!! 4-May-15!
34 Starter code: IniCalizaCon (A modified version of the original Matlab implementacon from Zendek Kalal) tldinit.m IniCalizes the LK tracker and also chooses posicve and negacve examples from the first frame for inicalizing the detector and Nearest Neighbor (NN) method TODO: IniCalize your detector based on the posicve and negacve examples from first frame Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-34!! 4-May-15!
35 Starter code: Process frame (A modified version of the original Matlab implementacon from Zendek Kalal) tldprocessframe.m Calls the LK tracker tldtracking.m to track densely sampled keypoints from bounding box Calls the trained detector to idencfy potencal object boxes in frame Integrates deteccon and tracking bounding boxes TODO: Modify the integrator to improve performance. The provided integrator might not be a good strategy for all video sequences Calls tldlearning to update detector and NN model Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-35!! 4-May-15!
36 Starter code: DetecCon (A modified version of the original Matlab implementacon from Zendek Kalal) tlddetection.m Calls the detector to idencfy candidate bounding boxes in the current frame TODO: Run your deteccon method on provided image patches tldnn.m Runs Nearest Neighbor model on the patches selected by detector from previous step TODO: Compute a confidence measure to determine how confident the NN is about each patch being a bbox Use Normalized Cross correlacon Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-36!! 4-May-15!
37 Starter code: Learning (A modified version of the original Matlab implementacon from Zendek Kalal) tldlearning.m Updates the deteccon model Calls tdltrainnn to update the NN model TODO: Train your deteccon method tldtrainnn.m TODO: Update stored posicve and negacve patches tld.pex and tld.nex based on newly seen posicve and negacve patches Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-37!! 4-May-15!
38 Starter code: +ve & - ve examples (A modified version of the original Matlab implementacon from Zendek Kalal) tldgeneratepositivedata.m Called by tldlearning.m and tldinit.m TODO: Choose posicve examples from current image based on overlap of the grid boxes with the tracked box from frame tldgeneratenegativedata.m Called by tldlearning.m and tldinit.m TODO: Choose negacve examples from current image based on overlap of the grid boxes with the tracked box from frame tldpatch2pattern.m TODO: Compute features from the given patches to be used by learning/deteccon/nn Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-38!! 4-May-15!
39 Starter code: Other UCls (Nothing todo) (A modified version of the original Matlab implementacon from Zendek Kalal) tlddisplay.m Plots the tracked bounding box on each image Shows the points tracked by LK tracker in blue Shows center points of patches selected by your detector in grey tldevaluate.m Evaluates tracking by compucng avg. overlap and avg. precision mex/bb_overlap.cpp: Computes overlap between bboxes mex/lk.cpp: Lucas Kenade tracker bbox/bb_cluster.m: Clusters bounding boxes bbox/bb_scan.m: generates a dense grid of bounding boxes in image Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-39!! 4-May-15!
40 What You Need To Do 1. Implement the TODO seccons in code 1. Learning / DetecCon method 2. Features used 3. PosiCve and NegaCve sampling strategy 4. Integrator to combine deteccon and tracking results 2. Measure performance with provided ground- truth for all videos (main.m) Sanity check: Our baseline TLD has average overlap=0.68, average precision=0.78. Should be able to get be\er performance. Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-40!! 4-May-15!
41 What You Need To Do Q: Do I have to use the Matlab starter code? A: No! But ask the TAs if you want to use another language. You might have to be careful about the LK tracking implementacon and integracon. Q: Do I need to turn in my code? A: Yes. There should be a script we can call that ll e.g. run your method on an image without any/ much modificacon. Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-41!! 4-May-15!
42 Outline TLD Review Error metrics Code Overview Project 2 Report Project 2 PresentaCons Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-42!! 4-May-15!
43 Project 2 Report Write- up template provided on website (link) Use CVPR LaTeX template No more than 5 pages Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-43!! 4-May-15!
44 Project 2 Report Rough seccons: 1. Overview of the field (online single object tracking) 2. The algorithm overview 3. Components implemented by you (Your contribucon) 1. Learning / DetecCon method 2. Features used 3. PosiCve and NegaCve sampling strategy 4. Integrator to combine deteccon and tracking results 4. Code README 5. Results 1. QuanCtaCve result for each sequence (ValidaCon + Test) 1. Avg. overlap, Avg. precision and Cme taken/frame 2. QualitaCve result with analysis 3. Error analysis for difficult sequences Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-44!! 4-May-15!
45 Project 2 Report Overview of the field What is the problem What is the general scope of methods we ve talked about in class Mini- summary of class papers Cite papers! Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-45!! 4-May-15!
46 Project 2 Report The algorithm overview Your understanding of how TLD works Why would just using a LK tracker fail? Why does only using deteccon/learning prohibicve? How do the tracker (T) and learning/deteccon (LD) interact? SuggesCons for improving the method! Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-46!! 4-May-15!
47 Project 2 Report Components implemented by you: Learning / DetecCon method Features used PosiCve and NegaCve sampling strategy Integrator to combine deteccon and tracking results MoCvate your choice for each component! Provide a quanctacve/qualitacve comparison with other possible model choices Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-47!! 4-May-15!
48 Project 2 Report Code A README for your code What are the key files/funccons (if you added addiconal files, explain them too) How can the TAs reproduce your results? Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-48!! 4-May-15!
49 Project 2 Report Results QuanCtaCve results Average precision per video sequence Average overlap per video sequence Time taken per frame to track object in the video For project 2, provide results separately for the validacon and test sets. QualitaCve results 2 interescng examples where your deteccon method succeeded and 2 examples where it failed Detailed error analysis for cases where it failed Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-49!! 4-May-15!
50 Extensions The assignment is open ended in terms of the features/learning/deteccon methods you choose to use Plenty of possibilices to try different methods J Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-50!! 4-May-15!
51 Possible Extensions Comparison of different features (patch2paxern) Binary features are usually fast to compute and give reasonable performance Try openly available implementacons of BRIEF, LBP, FREAK Dense features give good performance but are slower Resized patch aqer mean subtraccon (or whitening) HOG from resized patch Try different sampling and pooling strategies for features Densely sample the encre patch or use keypoints SpaCal pyramids for pooling Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-51!! 4-May-15!
52 Possible Extensions Comparison of different learning methods Slower batch trained classifiers such as linear SVM Faster online SVM, random ferns DetecCon strategy Run classifier densely on all grid bounding boxes Pre- select a smaller subset of good candidates to run classifier Data augmentagon for learning Warping/shiQing/noise addicon to posicve and negacves Mine only hard negacves for training classifiers Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-52!! 4-May-15!
53 Possible Extensions ExperimenGng with the integrator When to restart the LK tracker? How to weight the tracker and deteccon results? AdapCng the integrator method based on video properces Introducing priors to regularize the tracking Penalize sudden and large bbox transicons between frames Penalize sudden change in direccon of mocon Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-53!! 4-May-15!
54 TLD Review Error metrics Code Overview Outline Project 2 Report Project 2 PresentaCons Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-54!! 4-May-15!
55 Project 2 PresentaCons These happen the day before the report/code is due. Every team should submit 4-5 slides to Alex (alahi@stanford.edu) by 5 pm the day before (Sun May 10) Reminder: Teams of 1 or 2 people If two people, make sure both present! Randomly pick ~10 teams to present. Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-55!! 4-May-15!
56 Things to include in presentacon Important contribucons in your implementacon SubtleCes/things you didn t expect Important: 2 video results for your tracking Provide result on one video which is not from the provided dataset (Note: You may use ffmpeg to combine the output frames generated by the tracking method into a video) Any insights! Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-56!! 4-May-15!
57 Grading 35%: Technical Approach and Code Is your code correct? Do anything cool? 35%: Experimental EvaluaCon Performance, insights, thorough evaluacon 20%: Write- up Contains everything, forma\ed well, etc. 10%: Project PresentaCon Clarity, Content. Not counted if no presentacon in a week. Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-57!! 4-May-15!
58 Submi}ng Submit via CourseWork One submission per team We ll use cheacng- deteccon soqware Do not use the openly available TLD code! Cite any external code/library you use! Please don t make this an issue! Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-58!! 4-May-15!
59 Late Days You have 7, split between the three projects any way you want But your project presentacon itself scll needs to be on Cme (in class). Late days only apply to write- up/code submission Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-59!! 4-May-15!
60 Working in Groups You can work with up to one other person Shared code/report. We ll grade fairly regardless of team size Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-60!! 4-May-15!
61 Important Dates May 11(in class): PresentaCons May 12 (5 pm): Reports due Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-61!! 4-May-15!
62 Other QuesCons? Fei-Fei Li, Alexandre Alahi, Vignesh Ramanathan! Lecture 6-62!! 4-May-15!
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