A Recent Trend in Individual Counting Approach Using Deep Network

In video surveillance scheme, counting individuals is regarded as a crucial task. Of all the individual counting techniques in existence, the regression technique can offer enhanced performance under overcrowded area. However, this technique is unable to specify the details of counting individual such that it fails in locating the individual. On contrary, the density map approach is very effective to overcome the counting problems in various situations such as heavy overlapping and low resolution. Nevertheless, this approach may break down in cases when only the heads of individuals appear in video scenes, and it is also restricted to the feature’s types. The popular technique to obtain the pertinent information automatically is Convolutional Neural Network (CNN). However, the CNN based counting scheme is unable to sufficiently tackle three difficulties, namely, distributions of non-uniform density, changes of scale and variation of drastic scale. In this study, we cater a review on current counting techniques which are in correlation with deep net in different applications of crowded scene. The goal of this work is to specify the effectiveness of CNN applied on popular individuals counting approaches for attaining higher precision results

Multi-Stream Networks and Ground-Truth Generation for Crowd Counting

Crowd scene analysis has received a lot of attention recently due to the wide variety of applications, for instance, forensic science, urban planning, surveillance and security. In this context, a challenging task is known as crowd counting, whose main purpose is to estimate the number of people present in a single image. A Multi-Stream Convolutional Neural Network is developed and evaluated in this work, which receives an image as input and produces a density map that represents the spatial distribution of people in an end-to-end fashion. In order to address complex crowd counting issues, such as extremely unconstrained scale and perspective changes, the network architecture utilizes receptive fields with different size filters for each stream. In addition, we investigate the influence of the two most common fashions on the generation of ground truths and propose a hybrid method based on tiny face detection and scale interpolation. Experiments conducted on two challenging datasets, UCF-CC-50 and ShanghaiTech, demonstrate that using our ground truth generation methods achieves superior results.Comment: https://github.com/RQuispeC/multi-stream-crowd-counting-extended , The International Journal of Electrical and Computer Engineering Systems 202

Large-Scale Mapping of Human Activity using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.Comment: Accepted at ACM SIGSPATIAL 201

Deep learning in crowd counting: A survey

Counting high-density objects quickly and accurately is a popular area of research. Crowd counting has significant social and economic value and is a major focus in artificial intelligence. Despite many advancements in this field, many of them are not widely known, especially in terms of research data. The authors proposed a three-tier standardised dataset taxonomy (TSDT). The Taxonomy divides datasets into small-scale, large-scale and hyper-scale, according to different application scenarios. This theory can help researchers make more efficient use of datasets and improve the performance of AI algorithms in specific fields. Additionally, the authors proposed a new evaluation index for the clarity of the dataset: average pixel occupied by each object (APO). This new evaluation index is more suitable for evaluating the clarity of the dataset in the object counting task than the image resolution. Moreover, the authors classified the crowd counting methods from a data-driven perspective: multi-scale networks, single-column networks, multi-column networks, multi-task networks, attention networks and weak-supervised networks and introduced the classic crowd counting methods of each class. The authors classified the existing 36 datasets according to the theory of three-tier standardised dataset taxonomy and discussed and evaluated these datasets. The authors evaluated the performance of more than 100 methods in the past five years on different levels of popular datasets. Recently, progress in research on small-scale datasets has slowed down. There are few new datasets and algorithms on small-scale datasets. The studies focused on large or hyper-scale datasets appear to be reaching a saturation point. The combined use of multiple approaches began to be a major research direction. The authors discussed the theoretical and practical challenges of crowd counting from the perspective of data, algorithms and computing resources. The field of crowd counting is moving towards combining multiple methods and requires fresh, targeted datasets. Despite advancements, the field still faces challenges such as handling real-world scenarios and processing large crowds in real-time. Researchers are exploring transfer learning to overcome the limitations of small datasets. The development of effective algorithms for crowd counting remains a challenging and important task in computer vision and AI, with many opportunities for future research.BHF, AA/18/3/34220Hope Foundation for Cancer Research, RM60G0680GCRF, P202PF11;Sino‐UK Industrial Fund, RP202G0289LIAS, P202ED10, P202RE969Data Science Enhancement Fund, P202RE237Sino‐UK Education Fund, OP202006Fight for Sight, 24NN201Royal Society International Exchanges Cost Share Award, RP202G0230MRC, MC_PC_17171BBSRC, RM32G0178B

Multi-Stream Networks and Ground Truth Generation for Crowd Counting

Crowd scene analysis has received a lot of attention recently due to a wide variety of applications, e.g., forensic science, urban planning, surveillance and security. In this context, a challenging task is known as crowd counting [1–6], whose main purpose is to estimate the number of people present in a single image. A multi-stream convolutional neural network is developed and evaluated in this paper, which receives an image as input and produces a density map that represents the spatial distribution of people in an end-to-end fashion. In order to address complex crowd counting issues, such as extremely unconstrained scale and perspective changes, the network architecture utilizes receptive fields with different size filters for each stream. In addition, we investigate the influence of the two most common fashions on the generation of ground truths and propose a hybrid method based on tiny face detection and scale interpolation. Experiments conducted on two challenging datasets, UCF-CC-50 and ShanghaiTech, demonstrate that the use of our ground truth generation methods achieves superior results

PeopleNet: A Novel People Counting Framework for Head-Mounted Moving Camera Videos

Traditional crowd counting (optical flow or feature matching) techniques have been upgraded to deep learning (DL) models due to their lack of automatic feature extraction and low-precision outcomes. Most of these models were tested on surveillance scene crowd datasets captured by stationary shooting equipment. It is very challenging to perform people counting from the videos shot with a head-mounted moving camera; this is mainly due to mixing the temporal information of the moving crowd with the induced camera motion. This study proposed a transfer learning-based PeopleNet model to tackle this significant problem. For this, we have made some significant changes to the standard VGG16 model, by disabling top convolutional blocks and replacing its standard fully connected layers with some new fully connected and dense layers. The strong transfer learning capability of the VGG16 network yields in-depth insights of the PeopleNet into the good quality of density maps resulting in highly accurate crowd estimation. The performance of the proposed model has been tested over a self-generated image database prepared from moving camera video clips, as there is no public and benchmark dataset for this work. The proposed framework has given promising results on various crowd categories such as dense, sparse, average, etc. To ensure versatility, we have done self and cross-evaluation on various crowd counting models and datasets, which proves the importance of the PeopleNet model in adverse defense of society

Why Existing Multimodal Crowd Counting Datasets Can Lead to Unfulfilled Expectations in Real-World Applications

More information leads to better decisions and predictions, right? Confirming this hypothesis, several studies concluded that the simultaneous use of optical and thermal images leads to better predictions in crowd counting. However, the way multimodal models extract enriched features from both modalities is not yet fully understood. Since the use of multimodal data usually increases the complexity, inference time, and memory requirements of the models, it is relevant to examine the differences and advantages of multimodal compared to monomodal models. In this work, all available multimodal datasets for crowd counting are used to investigate the differences between monomodal and multimodal models. To do so, we designed a monomodal architecture that considers the current state of research on monomodal crowd counting. In addition, several multimodal architectures have been developed using different multimodal learning strategies. The key components of the monomodal architecture are also used in the multimodal architectures to be able to answer whether multimodal models perform better in crowd counting in general. Surprisingly, no general answer to this question can be derived from the existing datasets. We found that the existing datasets hold a bias toward thermal images. This was determined by analyzing the relationship between the brightness of optical images and crowd count as well as examining the annotations made for each dataset. Since answering this question is important for future real-world applications of crowd counting, this paper establishes criteria for a potential dataset suitable for answering whether multimodal models perform better in crowd counting in general