Learning Independent Instance Maps for Crowd Localization

Accurately locating each head's position in the crowd scenes is a crucial task in the field of crowd analysis. However, traditional density-based methods only predict coarse prediction, and segmentation/detection-based methods cannot handle extremely dense scenes and large-range scale-variations crowds. To this end, we propose an end-to-end and straightforward framework for crowd localization, named Independent Instance Map segmentation (IIM). Different from density maps and boxes regression, each instance in IIM is non-overlapped. By segmenting crowds into independent connected components, the positions and the crowd counts (the centers and the number of components, respectively) are obtained. Furthermore, to improve the segmentation quality for different density regions, we present a differentiable Binarization Module (BM) to output structured instance maps. BM brings two advantages into localization models: 1) adaptively learn a threshold map for different images to detect each instance more accurately; 2) directly train the model using loss on binary predictions and labels. Extensive experiments verify the proposed method is effective and outperforms the-state-of-the-art methods on the five popular crowd datasets. Significantly, IIM improves F1-measure by 10.4\% on the NWPU-Crowd Localization task. The source code and pre-trained models will be released at \url{https://github.com/taohan10200/IIM}

Fusion-layer-based machine vision for intelligent transportation systems/

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 307-317).Environment understanding technology is very vital for intelligent vehicles that are expected to automatically respond to fast changing environment and dangerous situations. To obtain perception abilities, we should automatically detect static and dynamic obstacles, and obtain their related information, such as, locations, speed, collision/occlusion possibility, and other dynamic current/historic information. Conventional methods independently detect individual information, which is normally noisy and not very reliable. Instead we propose fusion-based and layered-based information-retrieval methodology to systematically detect obstacles and obtain their location/timing information for visible and infrared sequences. The proposed obstacle detection methodologies take advantage of connection between different information and increase the computational accuracy of obstacle information estimation, thus improving environment understanding abilities, and driving safety.by Yajun Fang.Ph.D