107 research outputs found
Vehicle make and model recognition using bag of expressions
No full textThis article belongs to the Section Intelligent SensorsVehicle make and model recognition (VMMR) is a key task for automated vehicular surveillance (AVS) and various intelligent transport system (ITS) applications. In this paper, we propose and study the suitability of the bag of expressions (BoE) approach for VMMR-based applications. The method includes neighborhood information in addition to visual words. BoE improves the existing power of a bag of words (BOW) approach, including occlusion handling, scale invariance and view independence. The proposed approach extracts features using a combination of different keypoint detectors and a Histogram of Oriented Gradients (HOG) descriptor. An optimized dictionary of expressions is formed using visual words acquired through k-means clustering. The histogram of expressions is created by computing the occurrences of each expression in the image. For classification, multiclass linear support vector machines (SVM) are trained over the BoE-based features representation. The approach has been evaluated by applying cross-validation tests on the publicly available National Taiwan Ocean University-Make and Model Recognition (NTOU-MMR) dataset, and experimental results show that it outperforms recent approaches for VMMR. With multiclass linear SVM classification, promising average accuracy and processing speed are obtained using a combination of keypoint detectors with HOG-based BoE description, making it applicable to real-time VMMR systems.Muhammad Haroon Yousaf received funding from the Higher Education Commission, Pakistan
for Swarm Robotics Lab under the National Centre for Robotics and Automation (NCRA). The authors also acknowledge support from the Directorate of ASR& TD, University of Engineering and Technology Taxila, Pakistan
Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) model for human action recognition
No full textThis article belongs to the Section Intelligent SensorsHuman action recognition (HAR) has emerged as a core research domain for video understanding and analysis, thus attracting many researchers. Although significant results have been achieved in simple scenarios, HAR is still a challenging task due to issues associated with view independence, occlusion and inter-class variation observed in realistic scenarios. In previous research efforts, the classical bag of visual words approach along with its variations has been widely used. In this paper, we propose a Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) model for human action recognition without compromising the strengths of the classical bag of visual words approach. Expressions are formed based on the density of a spatio-temporal cube of a visual word. To handle inter-class variation, we use class-specific visual word representation for visual expression generation. In contrast to the Bag of Expressions (BoE) model, the formation of visual expressions is based on the density of spatio-temporal cubes built around each visual word, as constructing neighborhoods with a fixed number of neighbors could include non-relevant information making a visual expression less discriminative in scenarios with occlusion and changing viewpoints. Thus, the proposed approach makes the model more robust to occlusion and changing viewpoint challenges present in realistic scenarios. Furthermore, we train a multi-class Support Vector Machine (SVM) for classifying bag of expressions into action classes. Comprehensive experiments on four publicly available datasets: KTH, UCF Sports, UCF11 and UCF50 show that the proposed model outperforms existing state-of-the-art human action recognition methods in term of accuracy to 99.21%, 98.60%, 96.94 and 94.10%, respectively.Sergio A. Velastin is grateful for funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement N° 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, Cultura y Deporte (CEI-15-17) and Banco Santander. Muhammad Haroon Yousaf received funding from the Higher Education Commission, Pakistan for Swarm Robotics Lab under the National Centre for Robotics and Automation (NCRA). The authors also acknowledge support from the Directorate of ASR&TD, University of Engineering and Technology Taxila, Pakistan
زندانی ادب کے مفاہیم و رجحانات.......تجزیاتی مطالعہ
No full textLiterature is the mirror of the outward and inward society. When there is an atmosphere of restriction in society, people do not have the freedom to live as they wish. The literature created in this atmosphere describes all the oppressors and restrictions that took place in this era. Prisoner literature is a type of literature. Although it has not yet found the place and status in literature have. Notable aspects of prisoner literature include the historical aspect, the social and cultural aspect, and psychological aspect. Concept of crime and punishment begin with the start of human life on earth. Sometimes innocent people should also imprison. Hazrat Yousaf was innocent but remained in prison .Our Holy prophet Hazrat Muhammad (PBUH) remained in Shab-e-Abi Talib for three years. In Karbala after the shahadat of Hazrat Imam Hussain his four years old youngest daughter lost her life in prison. Imam Abu Hanifa, Imam Tamia should face hardships in prison. Imam Hanbal also faces troubles and hardships in jail. Imam Mousa Kazim spends fourteen years in prison in regimen of Haroon Rasheed. There are many countless examples in human history when those who raised the voice of truth were hanged. But the voice of truth could not be pressed by anyone
Multiple Batches of Motion History Images (MB-MHIs) for Multi-view Human Action Recognition
No full textThe recognition of human actions recorded in a multi-camera environment faces the challenging issue of viewpoint variation. Multi-view methods employ videos from different views to generate a compact view-invariant representation of human actions. This paper proposes a novel multi-view human action recognition approach that uses multiple low-dimensional temporal templates and a reconstruction-based encoding scheme. The proposed approach is based upon the extraction of multiple 2D motion history images (MHIs) of human action videos over non-overlapping temporal windows, constructing multiple batches of motion history images (MB-MHIs). Then, two kinds of descriptions are computed for these MHIs batches based on (1) a deep residual network (ResNet) and (2) histogram of oriented gradients (HOG) to effectively quantify a change in gradient. ResNet descriptions are average pooled at each batch. HOG descriptions are processed independently at each batch to learn a class-based dictionary using a K-spectral value decomposition algorithm. Later, the sparse codes of feature descriptions are obtained using an orthogonal matching pursuit approach. These sparse codes are average pooled to extract encoded feature vectors. Then, encoded feature vectors at each batch are fused to form a final view-invariant feature representation. Finally, a linear support vector machine classifier is trained for action recognition. Experimental results are given on three versions of a multi-view dataset: MuHAVi-8, MuHAVi-14, and MuHAVi-uncut. The proposed approach shows promising results when tested for a novel camera. Results on deep features indicate that action representation by MB-MHIs is more view-invariant than single MHIs.Muhammad Haroon Yousaf has received funding from Higher Education Commission, Pakistan, for Swarm Robotics Lab under National Centre for Robotics and Automation (NCRA). S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development, and demonstration under Grant Agreement No. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509), and Banco Santander. The authors also acknowledge support from the Directorate of ASR&TD, University of Engineering and Technology, Taxila, Pakistan, and of Nvidia Corporation for its donation of GPU equipment
A generic passive image forgery detection scheme using local binary pattern with rich models
No full textA Novel FDLSR-Based Technique for View-Independent Vehicle Make and Model Recognition
No full textVehicle make and model recognition (VMMR) is an important aspect of intelligent transportation systems (ITS). In VMMR systems, surveillance cameras capture vehicle images for real-time vehicle detection and recognition. These captured images pose challenges, including shadows, reflections, changes in weather and illumination, occlusions, and perspective distortion. Another significant challenge in VMMR is the multiclass classification. This scenario has two main categories: (a) multiplicity and (b) ambiguity. Multiplicity concerns the issue of different forms among car models manufactured by the same company, while the ambiguity problem arises when multiple models from the same manufacturer have visually similar appearances or when vehicle models of different makes have visually comparable rear/front views. This paper introduces a novel and robust VMMR model that can address the above-mentioned issues with accuracy comparable to state-of-the-art methods. Our proposed hybrid CNN model selects the best descriptive fine-grained features with the help of Fisher Discriminative Least Squares Regression (FDLSR). These features are extracted from a deep CNN model fine-tuned on the fine-grained vehicle datasets Stanford-196 and BoxCars21k. Using ResNet-152 features, our proposed model outperformed the SVM and FC layers in accuracy by 0.5% and 4% on Stanford-196 and 0.4 and 1% on BoxCars21k, respectively. Moreover, this model is well-suited for small-scale fine-grained vehicle datasets
SHAKE IT OFF: ESTABLISHING A TEEN SUPPORT GROUP AT THE MUHAMMAD ALI PARKINSON'S CENTER
No full textabstract: This thesis discusses the experiences of starting and building a support group for teenagers who have a loved one with Parkinson's Disease. One of the goals of this thesis was to share our experiences with the staff at the Muhammad Ali Parkinson's Center, and the teenagers who will be taking over this group in the future. We discuss why we wanted to start the group, how it's foundation was built, and the challenges we faced and overcame. This is done by highlighting three significant group meetings, and various implications. Transportation, funding, and other issues are discussed
- …
