Anomaly detection with a moving camera using multiscale video analysis

Carvalho, Giovanna; Thomaz, Lucas A.; Silva, Allan F. da; Silva, Eduardo A. B. da; Netto, Sérgio L.

doi:10.1007/s11045-018-0558-4

Cited by 13 publications

(13 citation statements)

References 41 publications

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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%

“…In order to compare the corresponding aligned frames, Ref. [10] used normalized cross-correlation (NCC) between the two images and applied multi-size NCC for different size detection objects. On the other hand, high resolution signals can be recovered from observed low-dimensional signals.…”

Section: Real-time Problem Of Object Detectionmentioning

confidence: 99%

“…The comparison between STC-mc [17], DAOMC [11], MCBS [12], mcRoSuRe-A [7] and ADMULT [10] and multi-SASL algorithm is shown in Table 4. In order to better reflect the comparison, Fig.…”

Section: Comparison Of Time Performancementioning

confidence: 99%

“…[9] proposes a benchmark to test the target tracking method of high frame rate dataset in camera, which can effectively assess the performance of the method from two aspects of speed and accuracy. Recently, de Carvalho et al proposed a multi-resolution method to detect objects of different sizes from moving camera in real time and effectively [10].…”

Section: Introductionmentioning

confidence: 99%

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Moving Object Detection Using Sparse Approximation and Sparse Coding Migration

Li¹,

Hu²,

Zhao³

2020

KSII TIIS

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In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace. First, low-rank sparse decomposition is used to reduce the dimension of the data. Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets. And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time. According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas. Finally, experiments are carried out on VDAO datasets containing 59 sets of videos. The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.

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Section: Comparison Of Object Detection Methodsmentioning

confidence: 99%

Section: Low Rank and Sparse Matrix Decompositionmentioning

confidence: 99%

Section: Real-time Problem Of Object Detectionmentioning

confidence: 99%

“…The comparison between STC-mc [17], DAOMC [11], MCBS [12], mcRoSuRe-A [7] and ADMULT [10] and multi-SASL algorithm is shown in Table 4. In order to better reflect the comparison, Fig.…”

Section: Comparison Of Time Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Moving Object Detection Using Sparse Approximation and Sparse Coding Migration

Li¹,

Hu²,

Zhao³

2020

KSII TIIS

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Crime activities prediction system in video surveillance by an optimized deep learning framework

Kumar¹,

Reddy²

2022

Concurrency and Computation

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The video surveillance technology is used to find crime events in public places and capture live public events. Hence, detecting the criminalist before the crime actions is the most needed event to catch the criminalist. However, the presence of noise content in the trained video has raised the difficulties in crime specification by maximizing the complexity range of the data. To overcome this issue, this research has designed a novel lion-based deep belief neural paradigm (LbDBNP) to identify criminals by their activities and handling tools. Initially, three types of datasets were trained to the system then the training flaws were eliminated in the preprocessing layer. Hereafter, the cleaned data is imported to the classification module to detect the crime events present in the video. Subsequently, the designed model is implemented using the Python framework in Windows 10 platform. To evaluate the efficiency of the designed model, the attack is launched in the proposed model after those metrics are calculated. In addition, the robustness of the designed system is verified by three datasets, such as UCSDped1, UCSDped2, and avenue crime. Also, the key parameters of the designed model have been evaluated and compared with other existing schemes to verify the proposed model's robustness by achieving the finest outcomes.

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