Towards Perspective-Free Object Counting with Deep Learning

Zou

IEEE Trans. on Image Process.

2018

Abstract-Visual object counting (VOC) is an emerging area in computer vision which aims to estimate the number of objects of interest in a given image or video. Recently, object density based estimation method is shown to be promising for object counting as well as rough instance localization. However, the performance of this method tends to degrade when dealing with new objects and scenes. To address this limitation, we propose a manifold-based method for visual object counting (M-VOC), based on the manifold assumption that similar image patches share similar object densities. Firstly, the local geometry of a given image patch is represented linearly by its neighbors using a predefined patch training set, and the object density of this given image patch is reconstructed by preserving the local geometry using locally linear embedding. To improve the characterization of local geometry, additional constraints such as sparsity and non-negativity are also considered via regularization, nonlinear mapping, as well as kernel trick. Compared with the state-of-the-art VOC methods, our proposed M-VOC methods achieve competitive performance on seven benchmark datasets. Experiments verify that the proposed M-VOC methods have several favorable properties, such as robustness to the variation in the size of training dataset and image resolution, as often encountered in real-world VOC applications.

Section: A Related Workmentioning

confidence: 99%

Manifold-Based Visual Object Counting

Zou

IEEE Trans. on Image Process.

2018

“…Onoro-Rubio and Lopez-Sastre in [16] proposed Hydra CNN to addressed the scale issue by proposing a scale aware counting model by uses a pyramid of image patches extracted at multiple scales to perform the final density prediction. Zhang et al [17] proposed Multi-column Convolutional Neural Network (MCNN) architecture to extract features generated by filters of different scales to generate the final prediction for its crowd density map.…”

Section: Related Workmentioning

confidence: 99%

Crowd Counting Mapping to make a Decision

Faisal¹,

Sleit²,

Al-Sayyed³

2018

ijacsa

Abstract-Congestion typically occurs when the number of crowds exceeds the capacity of facilities. In some cases, when buildings have to be evacuated, people might be trapped in congestion and cannot escape from the building early enough which might even lead to stampedes. Crowd Congestion Mapping (CCM) is a system that enables organizations to find information about the crowd congestion in target places. This project provides the ability to make the right decision to determine the reasons that led to that and to do the appropriate procedures to avoid this from happening again by optimizing locations and dimensions of the emergency exits less congested path on the target places. The system collects crowd congestion data from the locations and makes it available to corporations via target map. The congestion is plotted on target place map, for example, the red line for highly congested location, the pink line for mildly congested location and green line for free flow of humans in the location.

Mathematical Problems in Engineering

“…These methods, more suitable for extensively crowded scenes, can also be divided into two catalogues, global counting regression methods [6][7][8] and density map regression methods [9][10][11][12][13][14].…”

Section: Crowd Countingmentioning

confidence: 99%

“…Inspired by combining the high-level semantic information and low-level detailed features, Boominathan et al [13] combined deep and shallow networks to construct Longshort CNN as density map regression network. Aiming to solve the multiscale problem of person size, Onoro-Rubio and López-Sastre [14] proposed Hydra-CNN, using a pyramid of image patches of multiple scales to train multiple networks and benefitting from the integration of multiple models. Another attempt to promote the counting accuracy by model ensemble is the Boost-CNN [24], which employed boosting to density map regression CNN model.…”

Section: Crowd Countingmentioning

confidence: 99%

“…(i) Location-based method including ACF (Aggregate Channel Feature) [3] (ii) Feature-based regression methods, including LBP [40] combined with Ridge Regression, LBP with LSSVM [41] regression, and Gabor [42] with LSSVM regression (iii) CNN based density map regression methods, including Patch-count CNN [25], Patch-multitask CNN [26], TSCCM [27], MCNN [12], Long-short CNN [13], and Hydra-CNN [14].…”

Section: Experiments Detailsmentioning

confidence: 99%

See 1 more Smart Citation

Low-Rank and Sparse Based Deep-Fusion Convolutional Neural Network for Crowd Counting

Tang

Pan

Zhou

2017

This paper proposes an accurate crowd counting method based on convolutional neural network and low-rank and sparse structure. To this end, we firstly propose an effective deep-fusion convolutional neural network to promote the density map regression accuracy. Furthermore, we figure out that most of the existing CNN based crowd counting methods obtain overall counting by direct integral of estimated density map, which limits the accuracy of counting. Instead of direct integral, we adopt a regression method based on low-rank and sparse penalty to promote accuracy of the projection from density map to global counting. Experiments demonstrate the importance of such regression process on promoting the crowd counting performance. The proposed low-rank and sparse based deep-fusion convolutional neural network (LFCNN) outperforms existing crowd counting methods and achieves the state-of-the-art performance.