Gesture-based video summarization

Kosmopoulos, Dimitrios; Doulamis, Anastasios; Doulamis, N.

doi:10.1109/icip.2005.1530618

Cited by 10 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The inference of meaning from low-level video cues is non-trivial and thus many approaches are specifc to domains such as sports, music, or news. Approaches for extracting key frames from a video include detecting gestures [34,35], color [25], motion activities [12,20] and specifc events, e.g., goals in sports or diferences between frames in surveillance videos [16]. Internal approaches are challenging for surgical videos which generally do not contain any easily detectable shot changes, events nor diferences in color.…”

Section: Video Summarizationmentioning

confidence: 99%

Surgical Video Summarization

Avellino

Nozari

Canlorbe

et al. 2021

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

While surgical videos are valuable support material for activities around surgery, their summarization demands great amounts of time from surgeons, resulting in the production of very few videos. We study the practices involving surgical video to motivate and inform the future design of tools for their summarization. Through interviews and observations in a feld study, we fnd that (1) video summaries provide an important support for surgery, being used for self-improvement, education, discussing cases, scientifc research, patient communication and as legal resources; (2) video summarization follows a process hindered by the loss of knowledge that originates during recording; and, (3) surgeons develop ad-hoc coordination strategies which involve using the video itself for articulation work, making it both the feld of work and coordination artifact. We discuss ways in which tools can facilitate capturing knowledge during live action using these strategies.CCS Concepts: • Human-centered computing → Empirical studies in HCI.

show abstract

Section: Video Summarizationmentioning

confidence: 99%

Surgical Video Summarization

Avellino

Nozari

Canlorbe

et al. 2021

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

show abstract

“…Within the field of gesture classification, several studies have previously used optical datasets and ML, using peripherals such as the Leap Motion and Kinect [ 19 ]. Other studies have used images and videos to classify gestures [ 20 ]. Other studies in this area have compared ML algorithms using the Kinect/optical peripherals [ 21 ] in order to find the most efficient model.…”

Section: Literature Reviewmentioning

confidence: 99%

New Interfaces and Approaches to Machine Learning When Classifying Gestures within Music

Rhodes

Allmendinger

Climent

2020

Entropy

View full text Add to dashboard Cite

Interactive music uses wearable sensors (i.e., gestural interfaces—GIs) and biometric datasets to reinvent traditional human–computer interaction and enhance music composition. In recent years, machine learning (ML) has been important for the artform. This is because ML helps process complex biometric datasets from GIs when predicting musical actions (termed performance gestures). ML allows musicians to create novel interactions with digital media. Wekinator is a popular ML software amongst artists, allowing users to train models through demonstration. It is built on the Waikato Environment for Knowledge Analysis (WEKA) framework, which is used to build supervised predictive models. Previous research has used biometric data from GIs to train specific ML models. However, previous research does not inform optimum ML model choice, within music, or compare model performance. Wekinator offers several ML models. Thus, we used Wekinator and the Myo armband GI and study three performance gestures for piano practice to solve this problem. Using these, we trained all models in Wekinator and investigated their accuracy, how gesture representation affects model accuracy and if optimisation can arise. Results show that neural networks are the strongest continuous classifiers, mapping behaviour differs amongst continuous models, optimisation can occur and gesture representation disparately affects model mapping behaviour; impacting music practice.

show abstract

“…In recent years, hand gesture recognition [1] has attracted a growing interest due to its applications in many different fields, such as human-computer interaction, robotics, computer gaming, automatic sign-language interpretation and so on. The problem was originally tackled by the computer vision community by means of images and videos [1,2]. More recently the introduction of low cost consumer depth cameras, like Time-Of-Flight cameras and Microsoft's Kinect TM [3], has opened the way to several different approaches that exploit the depth information acquired by these devices for improving gesture recognition performance.…”

Section: Introductionmentioning

confidence: 99%

Hand gesture recognition with leap motion and kinect devices

Marin

Dominio

Zanuttigh

2014

2014 IEEE International Conference on Image Processing (ICIP)

347

168

View full text Add to dashboard Cite

The recent introduction of novel acquisition devices like the Leap Motion and the Kinect allows to obtain a very informative description of the hand pose that can be exploited for accurate gesture recognition. This paper proposes a novel hand gesture recognition scheme explicitly targeted to Leap Motion data. An ad-hoc feature set based on the positions and orientation of the fingertips is computed and fed into a multi-class SVM classifier in order to recognize the performed gestures. A set of features is also extracted from the depth computed from the Kinect and combined with the Leap Motion ones in order to improve the recognition performance. Experimental results present a comparison between the accuracy that can be obtained from the two devices on a subset of the American Manual Alphabet and show how, by combining the two features sets, it is possible to achieve a very high accuracy in real-time

show abstract

Gesture-based video summarization

Cited by 10 publications

References 19 publications

Surgical Video Summarization

Surgical Video Summarization

New Interfaces and Approaches to Machine Learning When Classifying Gestures within Music

Hand gesture recognition with leap motion and kinect devices

Contact Info

Product

Resources

About