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Counting in High-Density Crowd Videos

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Presentation on theme: "Counting in High-Density Crowd Videos"β€” Presentation transcript:

1 Counting in High-Density Crowd Videos
Edgar Lopez Mentor: Dr. Haroon Idrees

2 Video Annotations Method Compute forward tracks.
Compute backward tracks. Match forward and backward tracks. Compute average tracks by using weighting system. t frame t+step frame

3 Video Annotations 𝑇 𝑖 = π‘ π‘‘π‘’π‘βˆ’π‘–+𝑑 𝑠𝑑𝑒𝑝 𝐹𝑇+ π‘–βˆ’π‘‘ 𝑠𝑑𝑒𝑝 𝐡𝑇 step = 6 t = 1
𝑇 𝑖 = π‘ π‘‘π‘’π‘βˆ’π‘–+𝑑 𝑠𝑑𝑒𝑝 𝐹𝑇+ π‘–βˆ’π‘‘ 𝑠𝑑𝑒𝑝 𝐡𝑇 step = 6 t = 1 i = 2 i = 3 i = 4 i = 5 i = 6 𝑇 3 = 4 6 𝐹𝑇 𝐡𝑇 𝑇 4 = 3 6 𝐹𝑇 𝐡𝑇 𝑇 5 = 2 6 𝐹𝑇 𝐡𝑇 𝑇 3 = 1 6 𝐹𝑇 𝐡𝑇 𝑇 2 = 6βˆ’2+1 6 𝐹𝑇+ 2βˆ’1 6 𝐡𝑇 𝑇 2 = 5 6 𝐹𝑇 𝐡𝑇

4 Video Annotation Results

5 Video Annotation Results

6 Video Annotation Results
Every 5th frame annotated

7 Video Annotation Results
Every 30th frame annotated

8 Crowd Segmentation in Images
Superpixels Region Size Regularizer value Density Map Patch size Graph Cuts Multi-label Energy Minimization Lambda (how much weight we add to the constraint that neighboring superpixels must have a similar density)

9 Crowd Segmentation in Images
1 Graph Cuts Multi-label Energy Minimization u = 𝑒 1 𝑒 2 𝑒 𝑒 𝑛 π’Š=𝟏 𝒏 ( 𝒙 π’Š βˆ’ 𝒖 π’Š ) 𝟐 +Ξ» 𝒋=𝟏 π’Ž ( 𝒙 π’Š βˆ’ 𝒙 𝒋 ) 𝟐 GCMex function gives a label to each superpixel πŸβˆ’ 𝒖 π’Š 𝒖 π’Š 𝑒 1 𝑒 2 𝑒 3 Ξ»(𝒖 π’Š βˆ’ 𝒖 𝒋 ) 𝟐 𝑒 4 𝑒 5 𝑒 6 𝑒 7 𝑒 8 𝑒 𝑛

10 Crowd Segmentation in Images
Zabih, Ramin D., Olga Veksler, and Yuri Boykov. "System and method for fast approximate energy minimization via graph cuts." U.S. Patent No. 6,744, Jun

11 Experiment #1 Region Size = 50, Regularizer = 3000, Patch size = 192, Lambda = 0.25

12 Experiment #1 Region Size = 50, Regularizer = 3000, Patch size = 192, Lambda = 0.25

13 Experiment #1 Region Size = 50, Regularizer = 3000, Patch size = 192, Lambda = 0.25

14 Experiment #1 Region Size = 50, Regularizer = 3000, Patch size = 192, Lambda = 0.25

15 Experiment #1 Region Size = 50, Regularizer = 3000, Patch size = 192, Lambda = 0.25

16 Experiment #1 Region Size = 50, Regularizer = 3000, Patch size = 192, Lambda = 0.25

17 Experiment #2 Same parameters except patch size. PS = 96 PS = 192

18 Experiment #2 Same parameters except patch size. PS = 96 PS = 192

19 Experiment #2 Same parameters except patch size. PS = 96 PS = 192

20 Crowd Segmentation in Images
Different image resolutions affect the parameters. Add appearance features to improve segmentation. Mean Intensity of superpixel HOG Features


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