Introduction
The outcomes from the lab are to be handed in as a .zip file that contains a report and
programs that show that you have completed the steps of the lab successfully. Details are given at the end of this sheet.
1. Getting Started
Download the file “ECS734Lab4.tar.gz” from HYPERLINK “http://www.eecs.qmul.ac.uk/~ioannisp/ecs734/ECS734Lab4.tar.gz” www.eecs.qmul.ac.uk/~ioannisp/ecs734/ECS734Lab4.tar.gz
and extract the contents in the directory ecs734/lab4.
Check first there are two sub-folders and one main file:
data -> stores data in .mat files
test_fea -> contains 30 test sequences (10 sequences/class) in the .mat format
lab4.m-> main executable Matlab file
Start Matlab. Use “cd
In Matlab, load the “Lab4.m” file in the matlab editor. You will need to edit the code in the file in order to complete the parts of the lab that are in red with underline.
We use the terms codebook and dictionary interchangeably in this document
2. Creating Voting map for each of the codewords
Note the codebook creation by k-means clustering of the Spatiotemporal interest point descriptors (STIPs) in the ism.m.
The voting map creation for all codewords is performed in the file Votemap.m. Make note of how we store votes for the centre of the object at the current frame and votes for the start and end of action as well as votes for the size of the object’s bounding box. Note and understand in file Votemap.m the datastructure DataStructureVotemap. Make sure that you understand how we store the scale (both spatial and temporal) at which the space_time interest point has been detected.
3. Localising actions in image sequences
The function ism_test_voting calculates the voting maps for a single image sequence. Calculate the Euclidean matrix between the dictionary elements and the descriptors extracted in an image sequence.
Write a function that implements the voting scheme for the following properties: a) the spatial centre of the action at the current frame, b) the start and the end of action and c) the width and the height of the bounding box of the action in the current frame. The details are given in houghvoting.m
4. Evaluation
Using the provided code in ism_test_voting.m and recall_prec_curve.m, plot the Recall precision curves for each class.
Assign each sequence to a class according to which hypothesis received the higher number of votes (hint: use the values of the matrix TP_FP_mat). Report the misclassification error, or build the confusion matrix.
5. Dictionary size
Perform the localisation experiment using a very small dictionary and report the precision – recall curves. Hint: Cluster the descriptors into a small number of clusters (e.g. 20).
Explain the drop in the performance.
6. Handing In
Create a folder that will contain:
• A .pdf report that contains the answers to the exercises, the answers to specific
questions, plots from experiments and program listings (including comments).
• The programs files
Create a .zip file and submit electronically.
IMPORTANT: Plagiarism (copying from other students, or copying the work of others without proper referencing) is cheating, and will not be tolerated.
IF TWO “FOLDERS” ARE FOUND TO CONTAIN IDENTICAL MATERIAL, BOTH WILL BE GIVEN A MARK OF ZERO.
7. Examination
You should be orally examined in one lab session after you have submitted your code and report. You should be prepared to explain the code that you wrote and critically evaluate the results that you obtained.
Last updated Ioannis Patras 22/02/2013
School of Electronic Engineering and Computer Science
ECS734 Computer Vision Systems
Lab 4: Part-based Action localisation