HMM-based Scheme for Smart Instructor Activity Recognition in a Lecture Room Environment

Raza, Asim; Yousaf, Muhammad Haroon; Sial, Hassan Ahmed; Raja, Gulistan

doi:10.6029/smartcr.2015.06.008

Cited by 9 publications

(6 citation statements)

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“…Peerevaluation is also useful but time-consuming and may be biased. Therefore, automation using video analysis is useful for teachers and instructors [31,32]. In this case study, input lecture video streams are captured in an uncontrolled realistic environment and the instructor action recognition methodology begins with foreground extraction and the computing of MHIs for each video.…”

Section: Case Study: Instructor Action Recognition In Lecture Room Scenario Using Dtmdmentioning

confidence: 99%

Deep temporal motion descriptor (DTMD) for human action recognition

Nida¹

2020

Turk J Elec Eng & Comp Sci

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Spatiotemporal features have significant importance in human action recognition, as they provide the actor's shape and motion characteristics specific to each action class. This paper presents a new deep spatiotemporal human action representation, the deep temporal motion descriptor (DTMD), which shares the attributes of holistic and deep learned features. To generate the DTMD descriptor, the actor's silhouettes are gathered into single motion templates by applying motion history images. These motion templates capture the spatiotemporal movements of the actor and compactly represent the human actions using a single 2D template. Then deep convolutional neural networks are used to compute discriminative deep features from motion history templates to produce the DTMD. Later, DTMD is used for learning a model to recognize human actions using a softmax classifier. The advantage of DTMD are that DTMD is automatically learned from videos and contains higher-dimensional discriminative spatiotemporal representations as compared to handcrafted features; DTMD reduces the computational complexity of human activity recognition as all the video frames are compactly represented as a single motion template; and DTMD works effectively for single and multiview action recognition. We conducted experiments on three challenging datasets: MuHAVI-Uncut, iXMAS, and IAVID-1. The experimental findings reveal that DTMD outperforms previous methods and achieves the highest action prediction rate on the MuHAVI-Uncut dataset.

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Section: Case Study: Instructor Action Recognition In Lecture Room Scenario Using Dtmdmentioning

confidence: 99%

Deep temporal motion descriptor (DTMD) for human action recognition

Nida¹

2020

Turk J Elec Eng & Comp Sci

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“…Teaching effectiveness is a fundamental concept in contemporary education, valued by academic institutions as a goal on their own right. Some researchers have explored human pose recognition techniques using handcrafted features for estimating the instructor's activities in the classroom [1][2][3][4] walking, writing, pointing towards the board, standing, and addressing and pointing towards presentations, respectively. Silhouette representation is often computationally less expensive but demands precise segmentation of human silhouettes for pose estimation and such techniques have primarily focused on handcrafted representations of spatial information.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Dollar et al [5] propose the mapping of 2D to 3D spatiotemporal interest points as cuboid descriptions for actions prediction, while Wang and Schmid [7] establish dense motion trajectories 2 Mathematical Problems in Engineering (iDT) by computing the camera movement information. There are various types of spatiotemporal features to generalize action recognition including, spatiotemporal features [1], 3D-SIFT [8], HOG3D [9], extended SURF [10], iDT [7], histogram of optical flow (HOF) [11], and motion boundary histogram (MBH) [12]. Describing the iDT with MBH, HOG, HOF have shown the better prediction of activities on benchmark datasets (UCF101 [13], HMDB [14] and THUMOS [15]).…”

Section: Introductionmentioning

confidence: 99%

Instructor Activity Recognition through Deep Spatiotemporal Features and Feedforward Extreme Learning Machines

Nida

Yousaf

Irtaza

et al. 2019

Mathematical Problems in Engineering

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Human action recognition has the potential to predict the activities of an instructor within the lecture room. Evaluation of lecture delivery can help teachers analyze shortcomings and plan lectures more effectively. However, manual or peer evaluation is time-consuming, tedious and sometimes it is difficult to remember all the details of the lecture. Therefore, automation of lecture delivery evaluation significantly improves teaching style. In this paper, we propose a feedforward learning model for instructor’s activity recognition in the lecture room. The proposed scheme represents a video sequence in the form of a single frame to capture the motion profile of the instructor by observing the spatiotemporal relation within the video frames. First, we segment the instructor silhouettes from input videos using graph-cut segmentation and generate a motion profile. These motion profiles are centered by obtaining the largest connected components and normalized. Then, these motion profiles are represented in the form of feature maps by a deep convolutional neural network. Then, an extreme learning machine (ELM) classifier is trained over the obtained feature representations to recognize eight different activities of the instructor within the classroom. For the evaluation of the proposed method, we created an instructor activity video (IAVID-1) dataset and compared our method against different state-of-the-art activity recognition methods. Furthermore, two standard datasets, MuHAVI and IXMAS, were also considered for the evaluation of the proposed scheme.

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“…Recently, some computer vision findings have been reported to automatically estimate instructor performance using pose, gesture and activity recognition [15,19,20]. In [15], visionbased instructor activity recognition uses silhouette representation to train a Hidden Markov Model (HMM). The system was able to identify five activities: walking, writing, pointing towards the board, standing and pointing towards presentations with a recognition accuracy of 90%.…”

Section: Introductionmentioning

confidence: 99%

“…Then morphological features were used to generate fuzzy rules for instructor activities recognition. Such techniques [15,19,20] use spatial data for activity recognition but have ignored temporal information and have had to resort to their own datasets because of the unavailability of standard activ-ity datasets for instructor evaluation. Consequently, there is a need to develop standard datasets for researchers to compare and improve results.…”

Section: Introductionmentioning

confidence: 99%

Bag of Deep Features for Instructor Activity Recognition in Lecture Room

Nida

Yousaf

Irtaza

et al. 2018

MultiMedia Modeling

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This research aims to explore contextual visual information in the lecture room, to assist an instructor to articulate the effectiveness of the delivered lecture. The objective is to enable a self-evaluation mechanism for the instructor to improve lecture productivity by understanding their activities. Teacher's effectiveness has a remarkable impact on uplifting students performance to make them succeed academically and professionally. Therefore, the process of lecture evaluation can significantly contribute to improve academic quality and governance. In this paper, we propose a vision-based framework to recognize the activities of the instructor for self-evaluation of the delivered lectures. The proposed approach uses motion templates of instructor activities and describes them through a Bag-of-Deep features (BoDF) representation. Deep spatio-temporal features extracted from motion templates are utilized to compile a visual vocabulary. The visual vocabulary for instructor activity recognition is quantized to optimize the learning model. A Support Vector Machine classifier is used to generate the model and predict the instructor activities. We evaluated the proposed scheme on a self-captured lecture room dataset, IAVID-1. Eight instructor activities: pointing towards the student, pointing towards board or screen, idle, interacting, sitting, walking, using a mobile phone and using a laptop, are recognized with an 85.41% accuracy. As a result, the proposed framework enables instructor activity recognition without human intervention.

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HMM-based Scheme for Smart Instructor Activity Recognition in a Lecture Room Environment

Cited by 9 publications

References 0 publications

Deep temporal motion descriptor (DTMD) for human action recognition

Deep temporal motion descriptor (DTMD) for human action recognition

Instructor Activity Recognition through Deep Spatiotemporal Features and Feedforward Extreme Learning Machines

Bag of Deep Features for Instructor Activity Recognition in Lecture Room

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