Browsing by Author "Camara Chavez, Guillermo"

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    A New Method for Static Video Summarization Using Local Descriptors and Video Temporal Segmentation
    (Scopus, 2013) Cayllahua Cahuina, Edward; Camara Chavez, Guillermo
    The continuous creation of digital video has caused an exponential growth of digital video content. To increase the usability of such large volume of videos, a lot of research has been made. Video summarization has been proposed to rapidly browse large video collections. To summarize any type of video, researchers have relied on visual features contained in frames. In order to extract these features, different techniques have used local or global descriptors. In this paper, we propose a method for static video summarization that can produce meaningful and informative video summaries. We perform an evaluation using over 100 videos in order to achieve a stronger position about the performance of local descriptors in semantic video summarization. Our experimental results show, with a confidence level of 99%, that our proposed method using local descriptors and temporal video segmentation produces better summaries than state of the art methods. We also demonstrate the importance of a more elaborate method for temporal video segmentation, improving the generation of summaries, achieving 10% improvement in accuracy. We also acknowledge a marginal importance of color information when using local descriptors to produce video summaries. © 2013 IEEE.
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    An enhanced triplet CNN based on body parts for person re-identificacion
    (IEEE Computer Society, 2018) Durand Espinoza, Jonathan; Camara Chavez, Guillermo; Hinojosa Torres, Geraldine
    Person re-identificacion consists of reidentificating person through a set of images that is taken by different camera views. Despite recent advances in this field, this problem still remains a challenge due to partial occlusions, changes in illumination, variation in human body poses. In this paper, we present an enhanced Triplet CNN based on body-parts for person re-identification (AETCNN). We design a new model able to learn local body-part features and integrate them to produce the final feature representation of each input person. In addition, to avoid over-fitting due to the small size of the dataset, we propose an improvement in triplet assignment to speed up the convergence and improve performance. Experiments show that our approach achieves very promising results in (CUHK01) dataset and we advance state of the art, improving most of the results of the state of the art with a simpler architecture, achieving 76.50% in rank 1. © 2017 IEEE.