Computer Vision problems applied to visual surveillance have been studied for several years aiming at finding accurate and efficient solutions, required to allow the execution of surveillance systems in real environments. The main goal of such systems is to analyze the scene focusing on the detection and recognition of suspicious activities performed by humans in the scene, so that the security personnel can pay closer attention to these preselected activities. To accomplish that, several problems have to be solved first, for instance background subtraction, person detection, tracking and re-identification, face recognition, and action recognition. Even though each of these problems have been researched in the past decades, they are hardly considered in a sequence, each one is usually solved individually. However, in a real surveillance scenarios, the aforementioned problems have to be solved in sequence considering only videos as the input. Aiming at the direction of evaluating approaches in more realistic scenarios, this work proposes a framework called Smart Surveillance Framework (SSF), to allow researchers to implement their solutions to the above problems as a sequence of processing modules that communicate through a shared memory.
Face recognition has been largely studied in past years. However, most of the related work focus on increasing accuracy and/or speed to test a single pair probe-subject. In this work, we present a novel method inspired by the success of locality sensing hashing (LSH) applied to large general purpose datasets and by the robustness provided by partial least squares (PLS) analysis when applied to large sets of feature vectors for face recognition. The result is a robust hashing method compatible with feature combination for fast computation of a short list of candidates in a large gallery of subjects. We provide theoretical support and practical principles for the proposed method that may be reused in further development of hash functions applied to face galleries. The proposed method is evaluated on the FERET and FRGCv1 datasets and compared to other methods in the literature. Experimental results show that the proposed approach is able to speedup 16 times compared to scanning all subjects in the face gallery.
Face identification plays an important role in biometrics and surveillance. However, before applying face id1entification methods in real scenarios, we have to determine whether the subject in a test sample is known (enrolled in the face gallery). In this work, we focus on approaches to determine whether a given face sample belongs to a subject enrolled in the face gallery. We show how the approaches can be combined with face identification methods so they can perform open-set face recognition. Among the five approaches described in this work, four are based on responses from the face identification, and one is based on comparisons between known samples and samples from an independent background set. The approaches differ on features explored in the data, scalability and accuracy. We evaluate the proposed approaches in two standard and challenging datasets for face recognition (FRGC and PubFig83). Results considering different number of enrolled subjects show which approach can be considered in scenarios where, for instance, one is interested in recognizing few wanted subjects.
Dedicadoà Jéssica e aos meus pais, Francisco e Terezinha. Dedicadoà memória de Celsa e Marli. ix First, I would like to thank deeply professor William Robson Schwartz for the outstanding orientation on my undergraduate and graduate studies. I would also like to thank professors Ewa Kijak, Guillaume Gravier and Silvio J. F. Guimarães for their intellectual and financial support without which this work would never be completed. I thank the members of the examination committee, professors Erickson R. do Nascimento and Carlos E. Thomaz, for the enrichment that they brought to this work. I am eternally thankful for all support that my parents gave me, allowing me to focus on research and studies. I am very grateful for my beloved Jessica I. C. Souza for all support and love that she gave me throughout my master. I am also very grateful for all members of the SSIG team, the NPDI lab and the Linkmedia team for kindly receiving me in such amiable way and for all my friends in Brazil:
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