Parallel multi-modal background modeling

Bloisi, Domenico Daniele; Pennisi, Andrea; Iocchi, Luca

doi:10.1016/j.patrec.2016.10.016

Cited by 15 publications

(6 citation statements)

References 32 publications

(32 reference statements)

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“…Alternatives do exist in the form of human segmentation algorithms [20,33], but these have a very significant speed trade-off (seconds per frame instead of tens of frames per second) and might not be adjustable if they happen to not work in some environment. As it is, a modest level of environmental control and the use of a robust and easily tunable BGS implementation such as IMBS-MT [6] can be highly effective.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

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Automatic high fidelity foot contact location and timing for elite sprinting

Evans

Colyer

Salo

et al. 2021

Machine Vision and Applications

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Making accurate measurements of human body motions using only passive, non-interfering sensors such as video is a difficult task with a wide range of applications throughout biomechanics, health, sports and entertainment. The rise of machine learning-based human pose estimation has allowed for impressive performance gains, but machine learning-based systems require large datasets which might not be practical for niche applications. As such, it may be necessary to adapt systems trained for more general-purpose goals, but this might require a sacrifice in accuracy when compared with systems specifically developed for the application. This paper proposes two approaches to measuring a sprinter’s foot-ground contact locations and timing (step length and step frequency), a task which requires high accuracy. The first approach is a learning-free system based on occupancy maps. The second approach is a multi-camera 3D fusion of a state-of-the-art machine learning-based human pose estimation model. Both systems use the same underlying multi-camera system. The experiments show the learning-free computer vision algorithm to provide foot timing to better than 1 frame at 180 fps, and step length accurate to 7 mm, while the system based on pose estimation achieves timing better than 1.5 frames at 180 fps, and step length estimates accurate to 20 mm.

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Section: Discussion Of Resultsmentioning

confidence: 99%

“…This paper stays with background subtraction, specifically, the IMBS-MT algorithm [6], which was chosen as it met the requirements for speed, shadow handling, intuitive tuning for the environment and an excellent reference imple- Fig. 2 Overall system structure.…”

Section: Detectionmentioning

confidence: 99%

Automatic high fidelity foot contact location and timing for elite sprinting

Evans

Colyer

Salo

et al. 2021

Machine Vision and Applications

View full text Add to dashboard Cite

show abstract

“…These metrics are presented in Table 2 that illustrate the obtained results comparing with two background modeling methods IMBS-MT [54] and [43] respectively. As shown, the proposed method succeed to modelized the background with good results compared to the other method in the most of dataset videos including HighwayI, Hall&Monitor, sallen, and Foliage.…”

Section: Action Recognition and Summarization Resultsmentioning

confidence: 99%

“…The challenge in video summarization is related to the video understanding and the classification of important sequences of the video, and the importance depends on the types of actions or objects that must be summarized. The complexity and variety of scenes in a video making the foundation of generic methods impossible [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

A combined multiple action recognition and summarization for surveillance video sequences

et al. 2020

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Human action recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies' silhouette, then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the Temporal Difference Map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training. Also, using the TDMap images we recognize the action using a proposed CNN model. Action summarization is performed for each detected person. The efficiency of the proposed approach is shown through the obtained results for mainly multi-action detection and recognition.

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“…These metrics are presented in Table 3 that illustrates the obtained results compared with two background modeling methods used in IMBS-MT [43] and [44], respectively. As shown, the proposed method succeeds to modelize the background with good results in comparison with other methods in the most dataset videos including HighwayI, Hall & Monitor, Snellen, and Foliage.…”

Section: Background Modeling Evaluationmentioning

confidence: 99%

Gait recognition for person re-identification

et al. 2020

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Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has unique characteristics that allow recognizing a person from a distance. However, human recognition via gait technique could be limited with the position of captured images or videos. Hence, this paper proposes a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of the gait first, and this is then followed with the recognition process, which is performed using convolutional neural networks. Herein, multitask convolutional neural network models and extracted gait energy images (GEIs) are used to estimate the angle and recognize the gait. GEIs are extracted by first detecting the moving objects, using background subtraction techniques. Training and testing phases are applied to the following three recognized datasets: CASIA-(B), OU-ISIR, and OU-MVLP. The proposed method is evaluated for background modeling using the Scene Background Modeling and Initialization (SBI) dataset. The proposed gait recognition method showed an accuracy of more than 98% for almost all datasets. Results of the proposed approach showed higher accuracy compared to obtained results of other methods result for CASIA-(B) and OU-MVLP and form the best results for the OU-ISIR dataset.

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Parallel multi-modal background modeling

Cited by 15 publications

References 32 publications

Automatic high fidelity foot contact location and timing for elite sprinting

Automatic high fidelity foot contact location and timing for elite sprinting

A combined multiple action recognition and summarization for surveillance video sequences

Gait recognition for person re-identification

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