1. REU Program 2019 Week 6
Alex Ruiz
Jyoti Kini

2. Outline Weak-supervision based Multi-Object Tracking Research Papers:
Neighbourhood Consensus Networks
MOT16: A Benchmark for Multi-Object Tracking
GMMCP
MOT17 Dataset Pre-processing Module
Fine-tuning Model
Complete Tracking Trajectory of an Object
Outline

3. Weak-supervision based Multi-Object Tracking
Design a MOT system with weakly supervised learning algorithm
Pixel-level object tracking in a sequence of frames
Intend to find reliable dense correspondences between a pair of images
Enhance the key point matches algorithm to get better tracklet association

4. Neighbourhood Consensus Networks

5. MOT16: A Benchmark for Multi-Object Tracking
MOT16 Benchmark with a fair evaluation metric
14 videos with different viewpoints, camera motions and weather conditions
Most of them are filmed in high resolution
Pedestrians, vehicles, and objects are annotated

6. Evaluation Measures Name Description True Positive TP
The actual annotated target (Inside Distance)
False Positive/False Alarm FP
The number of false positive (Outside Distance)
False Negative FN
Number of missed targets
False Alarms per Frame
FAF
The Number of False Alarms per Frame
Identity Switches
ID Sw
Number of identity switches
Mostly Tracked MT
Ratio of GT tracked for at least 80% of its life span
Mostly Lost ML
Ratio of GT tracked less than 20% of its life span
Multiple Object Tracking Accuracy
MOTA
Combines three error sources: FP, FN and ID Sw
Multiple Object Tracking Precision
MOTP
The misalignment between the annotated and the predicted bounding boxes

7. GMMCP Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Enhanced data association where the input is a complete k-partite graph
Find K cliques (tracks) by selecting K nodes (tracklets) from every clusters
Uses Aggregated Dummy Nodes for handling occlusion
Low-level tracklets -> Mid-level tracklets -> Full Trajectories

8. MOT17 Dataset Pre-processing Module

9. Fine-tuning Model Using the Pf-Pascal and MOT17 Datasets:
20 Epochs/400 Image Size/16 Batch Size
Only Using MOT17 Dataset (Upscaled Version Model):
5 Epochs/640 Image Size/4 Batch Size

10. Fine-tuning Model Using the Pf-Pascal and MOT17 Datasets:
20 Epochs/400 Image Size/16 Batch Size

11. Pf-Pascal (20 Epochs/400 Image Size/16 Batch Size)

12. Mot17 (20 Epochs/400 Image Size/16 Batch Size)

13. Fine-tuning Model Only Using MOT17 Dataset (Upscaled Version Model):
5 Epochs/640 Image Size/4 Batch Size

14. 5 Epochs/640 Image Size/4 Batch Size

15. Complete Tracking Trajectory of an Object

16. Thank You!

17. 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 ).
Milan, A., Leal-Taixé, L., Reid, I., Roth, S. & Schindler, K. MOT16: A Benchmark for Multi-Object Tracking. arXiv: [cs], 2016., (arXiv: ).
Dehghan A, Modiri Assari S, Shah M. Gmmcp tracker: Globally optimal generalized maximum multi clique problem for multiple object tracking. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2015 (pp ).
References