画像からの顕著領域と人物の検出に関する研究

九州工業大学博士学位論文 学位記番号:工博甲第365号　学位授与年月日:平成26年3月25日1. General Introduction|2. Saliency detection using combined spatial non-redundancy and local appearance|3. Human detection using LBP-based patterns of oriented edges|4. General Conclusion|5. Appendices九州工業大学平成25年

Exploiting Feature Correlations by Brownian Statistics for People Detection and Recognition

International audienceCharacterizing an image region by its feature inter-correlations is a modern trend in computer vision. In this paper, we introduce a new image descriptor that can be seen as a natural extension of a covariance descriptor with the advantage of capturing nonlinear and non-monotone dependencies. Inspired from the recent advances in mathematical statistics of Brownian motion, we can express highly complex structural information in a compact and computationally efficient manner. We show that our Brownian covariance descriptor can capture richer image characteristics than the covariance descriptor. Additionally, a detailed analysis of the Brownian manifold reveals that in opposite to the classical covariance descriptor, the proposed descriptor lies in a relatively flat manifold, which can be treated as a Euclidean. This brings significant boost in the efficiency of the descriptor. The effectiveness and the generality of our approach is validated on two challenging vision tasks, pedestrian classification and person re-identification. The experiments are carried out on multiple datasets achieving promising results

Efficient Pedestrian Detection in Urban Traffic Scenes

Pedestrians are important participants in urban traffic environments, and thus act as an interesting category of objects for autonomous cars. Automatic pedestrian detection is an essential task for protecting pedestrians from collision. In this thesis, we investigate and develop novel approaches by interpreting spatial and temporal characteristics of pedestrians, in three different aspects: shape, cognition and motion. The special up-right human body shape, especially the geometry of the head and shoulder area, is the most discriminative characteristic for pedestrians from other object categories. Inspired by the success of Haar-like features for detecting human faces, which also exhibit a uniform shape structure, we propose to design particular Haar-like features for pedestrians. Tailored to a pre-defined statistical pedestrian shape model, Haar-like templates with multiple modalities are designed to describe local difference of the shape structure. Cognition theories aim to explain how human visual systems process input visual signals in an accurate and fast way. By emulating the center-surround mechanism in human visual systems, we design multi-channel, multi-direction and multi-scale contrast features, and boost them to respond to the appearance of pedestrians. In this way, our detector is considered as a top-down saliency system. In the last part of this thesis, we exploit the temporal characteristics for moving pedestrians and then employ motion information for feature design, as well as for regions of interest (ROIs) selection. Motion segmentation on optical flow fields enables us to select those blobs most probably containing moving pedestrians; a combination of Histogram of Oriented Gradients (HOG) and motion self difference features further enables robust detection. We test our three approaches on image and video data captured in urban traffic scenes, which are rather challenging due to dynamic and complex backgrounds. The achieved results demonstrate that our approaches reach and surpass state-of-the-art performance, and can also be employed for other applications, such as indoor robotics or public surveillance. In this thesis, we investigate and develop novel approaches by interpreting spatial and temporal characteristics of pedestrians, in three different aspects: shape, cognition and motion. The special up-right human body shape, especially the geometry of the head and shoulder area, is the most discriminative characteristic for pedestrians from other object categories. Inspired by the success of Haar-like features for detecting human faces, which also exhibit a uniform shape structure, we propose to design particular Haar-like features for pedestrians. Tailored to a pre-defined statistical pedestrian shape model, Haar-like templates with multiple modalities are designed to describe local difference of the shape structure. Cognition theories aim to explain how human visual systems process input visual signals in an accurate and fast way. By emulating the center-surround mechanism in human visual systems, we design multi-channel, multi-direction and multi-scale contrast features, and boost them to respond to the appearance of pedestrians. In this way, our detector is considered as a top-down saliency system. In the last part of this thesis, we exploit the temporal characteristics for moving pedestrians and then employ motion information for feature design, as well as for regions of interest (ROIs) selection. Motion segmentation on optical flow fields enables us to select those blobs most probably containing moving pedestrians; a combination of Histogram of Oriented Gradients (HOG) and motion self difference features further enables robust detection. We test our three approaches on image and video data captured in urban traffic scenes, which are rather challenging due to dynamic and complex backgrounds. The achieved results demonstrate that our approaches reach and surpass state-of-the-art performance, and can also be employed for other applications, such as indoor robotics or public surveillance

Training effective node classifiers for cascade classification

Extent: 23p. The final publication is available at www.springerlink.com: http://link.springer.com/article/10.1007/s11263-013-0608-1Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although there are a few reported methods addressing this requirement in the context of object detection, there is no principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such an algorithm here. We show that a special case of the biased minimax probability machine has the same formulation as the linear asymmetric classifier (LAC) of Wu et al (2005). We then design a new boosting algorithm that directly optimizes the cost function of LAC. The resulting totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on object detection verify the effectiveness of the proposed boosting algorithm as a node classifier in cascade object detection, and show performance better than that of the current state-of-the-art.Chunhua Shen, Peng Wang, Sakrapee Paisitkriangkrai, Anton van den Henge

Analisis dan Penerapan Perhitungan Orang Menggunakan Metode Histogram Of Oriented Gradients-Local Binary Pattern Dengan Deteksi Kepala-BahuStudi Kasus: Perhitungan Orang Dalam Kelas

Menghitung jumlah orang (people counting) yang berada dalam video merupakan salah satu fungsionalitas yang saat ini sedang dikembangkan para peneliti di dunia computer vision. People Counting memiliki berbagai macam manfaat seperti sistem pengawasan keamanan, estimasi jumlah kepadatan manusia, analisis bisnis di sebuah tempat, dan masih banyak lagi manfaatnya. Adapun studi kasus yang dipakai untuk pembuatan Tugas Akhir ini yaitu menghitung jumlah orang di dalam kelas dengan menggunakan kombinasi metode Histogram Of Oriented Gradients (HOG) dan Local Binary Pattern (LBP) untuk deteksi kepala dan bahu dari seorang individu. HOG-LBP digunakan untuk fitur ekstraksi untuk identifikasi objek. Kemudian perhitungan orang dilakukan dengan cakupan individu yang berada di dalam video. Keluaran dari sistem ini adalah sistem dapat menunjukkan jumlah orang dalam ruang kelas serta menunjukkan lokasi masing-masing dari individu tersebut berdasarkan kepala dan bahu. Dari hasil penelitian dengan menggunakan metode Multilevel HOG-LBP didapatkan rata-rata hasil akurasi orang duduk berjarak satu sebesar 93.53% dan rata-rata akurasi data orang duduk berdampingan sebesar 80.33%. Kata Kunci: people counting, histogram of oriented, local binary pattern, kepala,bah

Video content analysis for intelligent forensics

The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild

Contributions into holistic human action recognition.

Doctoral Degrees. University of KwaZulu-Natal, Durban.In this thesis we holistically investigate the interpretation of human actions in both still images and videos. Human action recognition is currently a research problem of great interest both in academia and industry due to its potential applications which include security surveillances, sports annotation, human-computer interaction, and robotics. Action recognition, being a process of labelling actions using sensory observations, can be defined as a sequence of movements engendered by a human during an executed task. Such a process, when considering visual observations, is quite challenging and faces issues such as background clutter, shadows, illumination variations, occlusions, changes in scale, changes in the person performing the action, and viewpoint variations. Although many approaches to development of human action recognition systems have been proposed in the literature, they focused more on recognition accuracy while ignoring the computational complexity accompanying the recognition process. However, a human action recognition system which is both effective and efficient and can be operated real-time is needed. Firstly, we review, evaluate and compare the most prominent state-of-the-art feature extraction representations categorized between handcrafted feature based techniques and deep learning feature based techniques. Secondly, we propose holistic approaches in each of the categories. The first holistic approach takes advantage of existing slope patterns in the motion history images, which are a simple two dimensional representation of video, and reduces the running time of action recognition. The second one based on circular derivative local binary patterns outperforms the LBP based state-of-the-art techniques and addresses the issues of dimensionality by producing feature descriptor with minimal dimension size with less compromise on the recognition accuracy. The third one introduces a preprocessing step in a proposed 2D-convolutional neural network to deal with the same issue of dimensionality differently in the deep learning techniques. Here the temporal dimension is embedded into motion history images before being learned by a two dimensional convolutional neural network. Thirdly, three datasets (JAFFE, KTH and Pedestrian Action dataset) were used to validate the proposed human action recognition models. Finally, we show that better performance in comparison to the state-of-the-art methods can be achieved using holistic feature based techniques.Author's Keywords : Human Action Recognition; Motion History Image; Circular Derivative Local BinaryPattern; Convolutional Neural Network; Facial Expression Recognition; Spatio-Temporal features