Sudden Event Recognition: A Survey

Suriani, Nor Surayahani; Hussain, Aini; Zulkifley, Mohd Asyraf

doi:10.3390/s130809966

Cited by 23 publications

(11 citation statements)

References 114 publications

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“…95 For the sake of the party, some sacrificed having children. Most of the women mentioned in the memoirs of these leaders were from the first generation.…”

Section: Women As Guerrilla Fighters: Challenges and Sacrificesmentioning

confidence: 99%

“…Their fighting spirit and readiness to face hardship during the Emergency explained why the title 'Srikandi' (heroines) was given to them and not to those from the second generation. 95 For the sake of the party, some sacrificed having children. Siti Norkiah and her husband Abu Samah kept postponing having a family as they were not willing to give away their babies for adoption.…”

Section: Women As Guerrilla Fighters: Challenges and Sacrificesmentioning

confidence: 99%

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Women in the Malayan Communist Party, 1942–89

Musa¹

2013

J. Southeast Asian stud.

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Women's involvement in the Malayan Communist Party (MCP) since its establishment in 1930 until they laid down their arms in 1989 contributed much to the strength of the party. Women in the MCP have been presented largely as nurses, cooks, seamstresses, couriers, and wireless/radio operators, but they went through hardship and danger and fought the same battles as the male guerrillas. A few even climbed to the top party posts through hard work, intelligence and personal sacrifice. This paper recovers the role of women in the Malayan communist movement during the Second World War, the Emergency and after by tracing the careers and lives of party heroines / female role models as well as some ordinary cadres. Major questions include the motivations of women who joined the MCP and the challenges they faced in their roles as propagandists, comrades, guerrilla fighters and in the communist villages. This investigation provides more insight into how the revolutionary struggle transformed these Malayan women.

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“…95 For the sake of the party, some sacrificed having children. Most of the women mentioned in the memoirs of these leaders were from the first generation.…”

Section: Women As Guerrilla Fighters: Challenges and Sacrificesmentioning

confidence: 99%

Section: Women As Guerrilla Fighters: Challenges and Sacrificesmentioning

confidence: 99%

Women in the Malayan Communist Party, 1942–89

Musa¹

2013

J. Southeast Asian stud.

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“…Visual surveillance is one of the major research areas in computer vision. After recording events by a visual sensor, such as a camera, obtaining detailed information of individual or crowd behavior is a challenging object in this area; automatic abnormal event detection is required to provide convenience, safety and an efficient lifestyle for humanity [ 1 ]. An abnormal event is defined as behavior deviating from what one expects.…”

Section: Introductionmentioning

confidence: 99%

Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

Wang

Chen

Zhou

et al. 2013

Sensors

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The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.

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“…• Sudden events can be defined as an abrupt, unintentional and unexpected change in the human body position that happens during a short period of observation, has not been observed before (i.e., was not present in the training dataset) and is unpredictable [33]. In the case of a home care assistance system, a sudden event refers specifically to a sudden fall by a patient or elderly person that requires immediate response.…”

Section: Tasks Of Harmentioning

confidence: 99%

Smartphone User Identity Verification Using Gait Characteristics

et al. 2016

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Smartphone-based biometrics offers a wide range of possible solutions, which could be used to authenticate users and thus to provide an extra level of security and theft prevention. We propose a method for positive identification of smartphone user's identity using user's gait characteristics captured by embedded smartphone sensors (gyroscopes, accelerometers). The method is based on the application of the Random Projections method for feature dimensionality reduction to just two dimensions. Then, a probability distribution function (PDF) of derived features is calculated, which is compared against known user PDF. The Jaccard distance is used to evaluate distance between two distributions, and the decision is taken based on thresholding. The results for subject recognition are at an acceptable level: we have achieved a grand mean Equal Error Rate (ERR) for subject identification of 5.7% (using the USC-HAD dataset). Our findings represent a step towards improving the performance of gait-based user identity verification technologies.

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Sudden Event Recognition: A Survey

Cited by 23 publications

References 114 publications

Women in the Malayan Communist Party, 1942–89

Women in the Malayan Communist Party, 1942–89

Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

Smartphone User Identity Verification Using Gait Characteristics

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