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

(cody_seibert@knights.ucf.edu, imran@eecs.ucf.edu)

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