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Crowd Counting by Estimation of Texture Repetition Cody Seibert, Imran Saleemi ( [email protected] , [email protected]) University of Central Florida. 1. Problem Count the number of people in an image of a crowd Difficulties High occlusion Low resolution - PowerPoint PPT Presentation
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2.1 Partitioning the Image Mitigates issues caused by foreshortening Increases accuracy of Fourier map due to local analysis of texture
repetition
Crowd Counting by Estimation of Texture RepetitionCody Seibert, Imran Saleemi
([email protected], [email protected])
University of Central Florida
1. Problem Count the number of people in an image of a crowd
Difficulties High occlusion Low resolution Varying lighting conditions Perspective Changes in viewpoint
2.3 Head Detection Confidence Map Train SVM for head detection using HOG Run the detector over each window location in image and plot
probability of classification Apply Gaussian smoothing
2.4 SIFT-based Texton Maps Run dense SIFT over image Cluster the descriptors using k-means For each cluster center, create a sift map by taking distance between
cluster center and dense sift descriptors Choose top clusters with least total distance and combine them
Intuition For a crowded scene, most image patches should be a member of very
few clusters which represent some part of a person
2. Proposed Method General automated method using texture analysis and
repetition
3. Results
2.2 Fourier-based Texture Repetition Map and Window Size Estimation
An example crowd imagewith approximately 1309 people
Fourier-based texture repetition map
Fourier-based texture repetition map
Head detection confidence map
Head detection confidence map SIFT-based texton mapsSIFT-based texton maps
Fusion of detection confidence maps
Fusion of detection confidence maps
Count and AverageCount and Average
Sum partitionsSum partitions
Partition ImagePartition Image
For each partition
Final count
Take gradient of Image
Convert to frequency domain using fast Fourier transform
Run non-maximum suppression to find strongest peaks
Generate different patterns usingpeaks near x and y axis
Take gradient of pattern and compare to original gradient
after alignment
Combine the best fitting patterns together to form the Fourier-based
texture repetition map
Estimating the Window Size Calculate distance between peak and center of spectrum Distance = Window width = image width / (2 x Distance) Window height = image height / (2 x Distance)
Head detection confidence map
2.5 Fusion of Confidence Maps and Counting Combine head detection confidence map, Fourier-based texture repetition
map, and SIFT-based confidence map Select a threshold for final map which maximizes the final count Repeat for each partition and sum all partition counts to obtain final
count
Image Ground Truth
SFH SH SF FH S H F
#2 630 650 674 758 655 713 667 812
#5 1309 1140 1241 1231 1169 1300 1303 1378
#6 1196 1259 1452 1428 1276 1616 1401 1478
#7 1667 1058 1277 1168 1044 1462 1148 1250
Average % Error14.5 14.24 18.9 14.7 15.3 12.8 20.7
The results currently show that the head detector remains the best form of crowd estimation
Top clusters and their SIFT-based confidence maps
Window size estimationWindow size estimation
Image partition