People counting using an overhead fisheye camera

As climate change concerns grow, the reduction of energy consumption is seen as one of many potential solutions. In the US, a considerable amount of energy is wasted in commercial buildings due to sub-optimal heating, ventilation and air conditioning that operate with no knowledge of the occupancy level in various rooms and open areas. In this thesis, I develop an approach to passive occupancy estimation that does not require occupants to carry any type of beacon, but instead uses an overhead camera with fisheye lens (360 by 180 degree field of view). The difficulty with fisheye images is that occupants may appear not only in the upright position, but also upside-down, horizontally and diagonally, and thus algorithms developed for typical side-mounted, standard-lens cameras tend to fail. As the top-performing people detection algorithms today use deep learning, a logical step would be to develop and train a new neural-network model. However, there exist no large fisheye-image datasets with person annotations to facilitate training a new model. Therefore, I developed two people-counting methods that leverage YOLO (version 3), a state-of-the-art object detection method trained on standard datasets. In one approach, YOLO is applied to 24 rotated and highly-overlapping windows, and the results are post-processed to produce a people count. In the other approach, regions of interest are first extracted via background subtraction and only windows that include such regions are supplied to YOLO and post-processed. I carried out extensive experimental evaluation of both algorithms and showed their superior performance compared to a benchmark method

Focus for Free in Density-Based Counting

This work considers supervised learning to count from images and their corresponding point annotations. Where density-based counting methods typically use the point annotations only to create Gaussian-density maps, which act as the supervision signal, the starting point of this work is that point annotations have counting potential beyond density map generation. We introduce two methods that repurpose the available point annotations to enhance counting performance. The first is a counting-specific augmentation that leverages point annotations to simulate occluded objects in both input and density images to enhance the network's robustness to occlusions. The second method, foreground distillation, generates foreground masks from the point annotations, from which we train an auxiliary network on images with blacked-out backgrounds. By doing so, it learns to extract foreground counting knowledge without interference from the background. These methods can be seamlessly integrated with existing counting advances and are adaptable to different loss functions. We demonstrate complementary effects of the approaches, allowing us to achieve robust counting results even in challenging scenarios such as background clutter, occlusion, and varying crowd densities. Our proposed approach achieves strong counting results on multiple datasets, including ShanghaiTech Part\_A and Part\_B, UCF\_QNRF, JHU-Crowd++, and NWPU-Crowd.Comment: 18 page

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