نتایج جستجو برای: crowded scenes

تعداد نتایج: 26030  

1999
B. A. Boghossian S. A. Velastin

Automated surveillance of crowded dynamic scenes requires prompt detection and classification of unusual activities as means of alerting operators to potentially dangerous situations as they arise. Motion is a strong cue that can be used to classify dynamic scenes and hence detect abnormal movements that can be related to critical situations. Here we propose a method to detect such unusual move...

Journal: :Journal of cognitive neuroscience 2009
Yaoda Xu

Many everyday activities, such as driving on a busy street, require the encoding of distinctive visual objects from crowded scenes. Given resource limitations of our visual system, one solution to this difficult and challenging task is to first select individual objects from a crowded scene (object individuation) and then encode their details (object identification). Using functional magnetic r...

Journal: :Soft Comput. 2015
Rafael Marcos Luque Baena Ezequiel López-Rubio Enrique Domínguez Esteban J. Palomo José M. Jerez

The obtaining of perfect foreground segmentation masks still remains as a challenging task in video surveillance systems, since errors in that initial stage could lead to misleadings in subsequent tasks as object tracking and behavior analysis. This work presents a novel methodology based on self-organizing neural networks and Gaussian distributions to detect unusual objects in the scene, and t...

Journal: :Science 2001
P Fries J H Reynolds A E Rorie R Desimone

In crowded visual scenes, attention is needed to select relevant stimuli. To study the underlying mechanisms, we recorded neurons in cortical area V4 while macaque monkeys attended to behaviorally relevant stimuli and ignored distracters. Neurons activated by the attended stimulus showed increased gamma-frequency (35 to 90 hertz) synchronization but reduced low-frequency (<17 hertz) synchroniza...

2016
Shuai Yi Hongsheng Li Xiaogang Wang

In this paper, a deep neural network (Behavior-CNN) is proposed to model pedestrian behaviors in crowded scenes, which has many applications in surveillance. A pedestrian behavior encoding scheme is designed to provide a general representation of walking paths, which can be used as the input and output of CNN. The proposed Behavior-CNN is trained with real-scene crowd data and then thoroughly i...

Journal: :EURASIP J. Adv. Sig. Proc. 2007
David Thirde Mark Borg Josep Aguilera Horst Wildenauer James M. Ferryman Martin Kampel

This paper describes a real-time multi-camera surveillance system that can be applied to a range of application domains. This integrated system is designed to observe crowded scenes and has mechanisms to improve tracking of objects that are in close proximity. The four component modules described in this paper are (i) motion detection using a layered background model, (ii) object tracking based...

2016
Jan Hendrik Hosang Rodrigo Benenson Bernt Schiele

Non-maximum suppression (NMS) is used in virtually all state-of-the-art object detection pipelines. While essential object detection ingredients such as features, classifiers and proposal methods have been extensively researched it it surprising how little work has aimed to systematically address NMS. The de-facto standard for NMS is based on greedy clustering with a fixed distance threshold, w...

2017
Yong Shean Chong Yong Haur Tay

We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos includi...

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