1. REU Program 2019 Week 3
Alex Ruiz
Jyoti Kini

2. Outline Weak-supervision based Multi-Object Tracking Research Papers
PyTorch Coding Exercise
Experimental Qualitative Results
Upcoming Schedule
Outline

3. Weak-supervision based Multi-Object Tracking
Design a MOT system with weakly supervised learning algorithm
Tackling the problem with pixel-level object tracking in a sequence of frames
Intend to find reliable dense correspondences between a pair of images
Reduce number of identity switches

4. Research Papers Neighbourhood Consensus Networks
Simple Online and Realtime Tracking with Deep Association Metric

5. Neighbourhood Consensus Networks

6. Simple Online and Realtime Tracking with Deep Association Metric
Reduces the number of identity switches with an association metric in which combines motion and appearance information
Integrate the Matching Cascade Algorithm to decrease the uncertainty associated with the object location when occluding for a longer period of time

7. PyTorch Coding Exercise
Dataset: MNIST (28, 28, 1)
Batch Size: 64
Learning Rate: 0.01
Filter Size: (5, 5)
Epoch: 10

8. Experimental Qualitative Results
Key-point Matching Module for MOT

9. Upcoming Schedule Walkthrough the MOT 2017 Dataset
Implementing a small module associated to the project
Understand the implementation for tracking evaluation metrics

10. Thank You!

11. Rocco I, Cimpoi M, Arandjelović R, Torii A, Pajdla T, Sivic J
Rocco I, Cimpoi M, Arandjelović R, Torii A, Pajdla T, Sivic J. Neighbourhood Consensus Networks. InAdvances in Neural Information Processing Systems 2018 (pp ).
Wojke N, Bewley A, Paulus D. Simple online and Realtime tracking with a deep association metric. In2017 IEEE International Conference on Image Processing (ICIP) 2017 Sep 17 (pp ). IEEE.
References