Cascade Classifiers and Saliency Maps Based People Detection

Aguilar, Wilbert G.; Luna, Marco A.; Moya, Julio F.; Abad, Vanessa; Ruiz, Hugo; Parra, Humberto; Lopez, William M.

doi:10.1007/978-3-319-60928-7_42

Cited by 18 publications

(12 citation statements)

References 37 publications

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“…where C i (which goes to saliency map) is a normalised feature map, w i represents the coefficient weights, and N is the number of integrated feature maps. The coefficient weights w i (see (15)), are usually calculated as follows [21,64]:…”

Section: Feature Fusionmentioning

confidence: 99%

Fusion of visual salience maps for object acquisition

Greenberg

Bensimon

Podeneshko

et al. 2020

IET Computer Vision

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The paradigm of visual attention has been widely investigated and applied to many computer vision applications. In this study, the authors propose a new saliency‐based visual attention algorithm applied to object acquisition. The proposed algorithm automatically extracts points of visual attention (PVA) in the scene, based on different feature saliency maps. Each saliency map represents a specific feature domain, such as textural, contrast, and statistical‐based features. A feature selection, based on probability of detection and false alarm rate and repeatability criteria, is proposed to choose the most efficient feature combination for saliency map. Motivated by the assumption that the extracted PVA represents the most visually salient regions in the image, they suggest using the visual attention approach for object acquisition. A comparison with other well‐known algorithms for point of interest detection shows that the proposed algorithm performs better. The proposed algorithm was successfully tested on synthetic, charge‐coupled device (CCD), and infrared (IR) images. Evaluation of the algorithm for object acquisition, based on ground truth, is carried out using synthetic images, which contain multiple examples of objects, with various sizes and brightness levels. A high probability of correct detection (greater than 90%) with a low false alarm rate (about 20 false alarms per image) was achieved.

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Section: Feature Fusionmentioning

confidence: 99%

Fusion of visual salience maps for object acquisition

Greenberg

Bensimon

Podeneshko

et al. 2020

IET Computer Vision

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show abstract

“…In general, the following factors affect the Wi-Fi positioning effect [19]: the absorption effects of Wi-Fi signals, the reflection and diffraction of Wi-Fi signals, the multipath propagation and shadow attenuation of Wi-Fi signals, the size of indoor areas, the changes in temperature and humidity in indoor environment sand the signal interference from other electronic devices. In addition, due to the lack of uniform rules and optimization of Wi-Fi, the network environment of the Wi-Fi AP is extremely complicated, and the number of manufacturers producing Wi-Fi APs and terminals is large, so the performance difference between devices is very obvious, which means that the various influencing factors must be considered in a specific position to improve the accuracy of positioning.…”

Section: Wi-fi Positioning Architecturementioning

confidence: 99%

Research on Wi-Fi indoor positioning in a smart exhibition hall based on received signal strength indication

Yang

Zheng

Liu

et al. 2019

J Wireless Com Network

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To improve the management of science and technology museums, this paper conducts an in-depth study on Wi-Fi (wireless fidelity) indoor positioning based on mobile terminals and applies this technology to the indoor positioning of a science and technology museum. The location fingerprint algorithm is used to study the offline acquisition and online positioning stages. The positioning flow of the location fingerprint algorithm is discussed, and the improvement of the location fingerprint algorithm is emphasized. The raw data of the RSSI (received signal strength indication) is preprocessed, which makes the location fingerprint data more effective and reliable, thus improving the positioning accuracy. Three different improvement strategies are proposed for the nearest neighbor classification algorithm: a balanced joint metric based on distance weighting and a compromise between the two. Then, in the experimental simulation, the positioning results and errors of the traditional KNN (k-nearest neighbor) algorithm and three improvement strategy algorithms are analyzed separately, and the effectiveness of the three improved strategy algorithms is verified by experiments.

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“…The research on the saliency model is a popular topic in computer vision and its objective is to simulate human visual attention as accurately as possible. Since the saliency model can effectively predict human visual attention on visual scenes (Veale, Hafed, and Yoshida 2017), it has been widely used in areas such as scene recognition (Zhang, Du, and Zhang 2014;Hu et al 2016), identification and tracking of people (Aguilar et al 2017b(Aguilar et al , 2017a, navigation (Wang and Tian 2011), detection and recognition of road signage (Zahabi et al 2017;Won, Lee, and Son 2008) and many more. The model was inspired by the following visual cognition mechanism; the visual world contains enormous amounts of information, and human visual attention is a scarce resource.…”

Section: Saliency Modelmentioning

confidence: 99%

Understanding and evaluating visual guidance quality inside passenger terminals - a cognitive and quantified approach

Xia

2019

Journal of Asian Architecture and Building Engineering

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Using the findings from visual cognitive psychology and computer vision, this study proposes a method that can predict how passenger visual attention to indoor visual guidance elements will affect the visual guidance quality inside passenger terminals. A saliency model is used to simulate the human visual attention, so as to understand how the visual guidance elements and visual noise are cognitively perceived by passengers. For every possible origin and destination node combination, the length and probability of the path that passengers are most likely to take (LP) are compared with the respective shortest path (SP). The overall evaluation of the terminal's visual guidance quality can be expressed by the Extra Walking Index. The validity of the developed evaluation method is verified and it is then applied in a case study. The method can be used as a supporting tool for architects to identify relevant architectural features in the design phase and optimize them accordingly; in addition, it can also provide existing passenger terminals with specific improvement suggestions.

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Cascade Classifiers and Saliency Maps Based People Detection

Cited by 18 publications

References 37 publications

Fusion of visual salience maps for object acquisition

Fusion of visual salience maps for object acquisition

Research on Wi-Fi indoor positioning in a smart exhibition hall based on received signal strength indication

Understanding and evaluating visual guidance quality inside passenger terminals - a cognitive and quantified approach

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