2018
DOI: 10.1007/s11045-018-0558-4
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Anomaly detection with a moving camera using multiscale video analysis

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Cited by 13 publications
(13 citation statements)
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“…We compared the performance of our proposed multi-SASL to the state-of-the-art mcRoSuRe-A [7], as well as the STC-mc [17], DAOMC [11], MCBS [12], and ADMULT [10] methods, and the result is shown in Table 1. Multi-SASL method outperforms other algorithms except ADMULT method.…”
Section: Comparison Of Object Detection Methodsmentioning
confidence: 99%
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“…We compared the performance of our proposed multi-SASL to the state-of-the-art mcRoSuRe-A [7], as well as the STC-mc [17], DAOMC [11], MCBS [12], and ADMULT [10] methods, and the result is shown in Table 1. Multi-SASL method outperforms other algorithms except ADMULT method.…”
Section: Comparison Of Object Detection Methodsmentioning
confidence: 99%
“…Jardim et al used subspace restoration and sparse decomposition to complete anomaly detection, and explored the low-rank similarity between reference video and target video as well as the sparseness of differences between target video soon afterwards. Recent study proposed introducing spatial information of object into object detection algorithm in subspace learning architecture [10]. Although low rank and sparse decomposition has prominent advantages in the detection of dynamic objects in moving camera, it has relatively high computational complexity and poor real-time performance.…”
Section: Low Rank and Sparse Matrix Decompositionmentioning
confidence: 99%
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