Video Indexing Using Face Appearance and Shot Transition Detection

Cazzato, Dario; Leo, Marco; Carcagnì, Pierluigi; Distante, Cosimo; Lorenzo-Navarro, Javier; Voos, Holger

doi:10.1109/iccvw.2019.00319

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Error rates in the range of 1-14% is achieved. A technique to determine indices of video clip is presented in [8]. A method to recognize music at low signal to noise ratio proposed in [9] helped for commercial owners in television domain.…”

Section: Literature Surveymentioning

confidence: 99%

An algorithm for enhancement of audio content classification

Bang¹,

Purandare²,

Ratnaparkhi³

2023

Bulletin EEI

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Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine set-up bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing rate (ZCR) and spectral roll frequency. From the experimentation results, it is observed that the proposed algorithm can achieve accuracy of 95.8% for 2 sec window length as compare with k-neighbor algorithm. A total enhancement of 11% is achieved with cross validation error of 29.6. A superior assortment of training fabric to extract few additional useful features can enhance accuracy further.

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Section: Literature Surveymentioning

confidence: 99%

An algorithm for enhancement of audio content classification

Bang¹,

Purandare²,

Ratnaparkhi³

2023

Bulletin EEI

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“…Since our method deals with sequences with camera shot transitions, we detect these to apply a different processing using the method proposed in [14], which analyzes the color distribution of consecutive frames to detect strong variations. Hence, after a shot transition is detected, the continuity of the sequence has been broken and the spatial and 2D pose analysis are not reliable.…”

Section: Camera Shots Transitions Detectionmentioning

confidence: 99%

Multi-Person Re-Identification Based on Face, Pose and Texture Analysis in Unconstrained Videos

Gallego

Slater

2020

2020 IEEE 21st International Conference on Computational Problems of Electrical Engineering (CPEE)

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We present a method for re-identification of images of multiple people that appear in 2D RGB video sequences. The method needs no initialization or supervision and works with unconstrained sequences that include camera shot transitions and strong visual variations. In order to preserve tracking along the frames, our method combines facial recognition, clothing texture analysis and pose detection to compute a distance between tracked people at frame t-1, and detected people at frame t. We use techniques based on Convolutional Neural Networks (CNNs) to extract this information from the people that appear in the images. The results obtained show that the proposed method achieves accurate tracking of the people even in those difficult sequences where faces appear occluded or people present similar textures.Index Terms-Multi-person re-identification, people tracking, Unconstrained tracking in videos

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