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WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems.

, , , and . AVSS, page 1-6. IEEE, (2018)

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Real-time Convolutional Networks for Depth-based Human Pose Estimation., , , and . CoRR, (2019)Efficient Convolutional Neural Networks for Depth-Based Multi-Person Pose Estimation., , , and . IEEE Trans. Circuits Syst. Video Technol., 30 (11): 4207-4221 (2020)WatchNet++: efficient and accurate depth-based network for detecting people attacks and intrusion., , , and . Mach. Vis. Appl., 31 (6): 41 (2020)MIRACLE at WebCLEF 2005: Combining Web Specific and Linguistic Information., , , and . CLEF, volume 4022 of Lecture Notes in Computer Science, page 869-872. Springer, (2005)Real-time Convolutional Networks for Depth-based Human Pose Estimation., , , and . IROS, page 41-47. IEEE, (2018)Aplicación de técnicas de extracción de información temporal a los sistemas de búsqueda de respuestas., , , and . Proces. del Leng. Natural, (2009)Residual Pose: A Decoupled Approach for Depth-based 3D Human Pose Estimation., , , and . IROS, page 10313-10318. IEEE, (2020)Efficient Convolutional Neural Networks for Depth-Based Multi-Person Pose Estimation., , , and . CoRR, (2019)Investigating Depth Domain Adaptation for Efficient Human Pose Estimation., , , and . ECCV Workshops (2), volume 11130 of Lecture Notes in Computer Science, page 346-363. Springer, (2018)MIRACLE's Ad-Hoc and Geographical IR Approaches for CLEF 2006., , , and . CLEF, volume 4730 of Lecture Notes in Computer Science, page 877-880. Springer, (2006)