Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM

Samuel, R. Dinesh Jackson; Fenil, E; Manogaran, Gunasekaran; Vivekananda, G. N.; Thanjaivadivel, T; Jeeva, Subbiah; Ahilan, A

doi:10.1016/j.comnet.2019.01.028

Cited by 141 publications

(28 citation statements)

References 10 publications

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“…Haiyun and Yizhe [17] developed a Hadoop platform for predicting game results which is an integrated learning and a comprehensive learning algorithms. Dinesh et al [18] proposed a real time violence detection framework for football stadium. HOG function was used to extract the features from the video frames in Spark environment.…”

Section: Sport Data Streamingmentioning

confidence: 99%

A Dockerized Big Data Architecture for Sports Analytics

Özgüven

Gönener

Eken

2021

Preprint

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The revolution of big data has also affected the area of sports analytics. Many big companies have started to see the benefits of combining sports analytics and big data to make a profit. Aggregating and processing big sport data from different sources becomes challenging if we rely on central processing techniques, which hurts the accuracy and the timeliness of the information. Distributed systems come to the rescue as a solution to these problems and the MapReduce paradigm is promising for large-scale data analytics. In this study, we present a big data architecture based on Docker containers in Apache Spark. We demonstrate the architecture on four data-intensive case studies including structured analysis, streaming, machine learning methods, and graph-based analysis in sport analytics, showing ease of use.

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Section: Sport Data Streamingmentioning

confidence: 99%

A Dockerized Big Data Architecture for Sports Analytics

Özgüven

Gönener

Eken

2021

Preprint

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“…The main intention of this algorithm is to train the feature with easier way by utilizing small parameters. 26 As illustrated in Figure 6, convolution network is one of the fully connected neural network in which it has consider the image in terms of m*m*r, ie, m is height and width of image and r is number of channels present in the image. During the learning process, Convnet is used to remove the noise in which kernel value must be selected with size n*n*q. N is denoted as the image smaller dimension value and q is same as the channel r, which is varied for every kernel value.…”

Section: Facial Features Trainingmentioning

confidence: 99%

LAMSTAR: For IoT‐based face recognition system to manage the safety factor in smart cities

Medapati¹,

Murthy²,

Sridhar

2019

Trans Emerging Tel Tech

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Digital transformation played a vital role in smart cities because of its ability to process the different data to providing sustainability, connectivity, and mobility to data effectively. According to the diver operational productivity, preserving public safety government law enforcement integrated that face recognition is more important in smart cities. Traditional face recognition system fails to predict the exact facial features that leads to reduce the facial recognition accuracy. The false facial point detection process maximizes the computation complexity. Therefore, in this work, effective and artificial intelligence internet of things-based facial expression detection system is implemented to predict and match the face from the database. Initially, the facial images are captured from the internet of things sensor device, which is processed by applying the Perona-Malik diffusion algorithm. Then, face location is cropped from the image, geometric face shape model is created for predicting the exact face from the template face image. From the face shape, different facial features are extracted from various region using Fisher linear discriminant analysis. The derived features are trained with the help of convolution network and the face recognition process is done by using the adaboost large memory storage and retrieval neural network. The network successfully recognizes the face from the template, which is used to eliminate the safety related risk in smart cities.

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“…In the past few years, DL approaches play a crucial role in analysing the big image data (Khoshboresh Masouleh and Shah-Hosseini, 2019a;Maggiori et al, 2017;Masouleh and Shah-Hosseini, 2018;Samuel R. et al, 2019). The DL approaches incorporate two influential concepts in an optimal big data analysis workflow for image data .…”

Section: Deep Learningmentioning

confidence: 99%

“…In the past few years, DL approaches play a crucial role in analysing the big image data [14]- [17]. The DL approaches incorporate two influential concepts in an optimal big data analysis workflow for image data [18].…”

Section: Deep Learningmentioning

confidence: 99%

Robust Building Footprint Extraction From Big Multi-Sensor Data Using Deep Competition Network

Khoshboresh-Masouleh¹,

Saradjian

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 ABSTRACT:Building footprint extraction (BFE) from multi-sensor data such as optical images and light detection and ranging (LiDAR) point clouds is widely used in various fields of remote sensing applications. However, it is still challenging research topic due to relatively inefficient building extraction techniques from variety of complex scenes in multi-sensor data. In this study, we develop and evaluate a deep competition network (DCN) that fuses very high spatial resolution optical remote sensing images with LiDAR data for robust BFE. DCN is a deep superpixelwise convolutional encoder-decoder architecture using the encoder vector quantization with classified structure. DCN consists of five encoding-decoding blocks with convolutional weights for robust binary representation (superpixel) learning. DCN is trained and tested in a big multi-sensor dataset obtained from the state of Indiana in the United States with multiple building scenes. Comparison results of the accuracy assessment showed that DCN has competitive BFE performance in comparison with other deep semantic binary segmentation architectures. Therefore, we conclude that the proposed model is a suitable solution to the robust BFE from big multi-sensor data.

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Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM

Cited by 141 publications

References 10 publications

A Dockerized Big Data Architecture for Sports Analytics

A Dockerized Big Data Architecture for Sports Analytics

LAMSTAR: For IoT‐based face recognition system to manage the safety factor in smart cities

Robust Building Footprint Extraction From Big Multi-Sensor Data Using Deep Competition Network

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