سیرت طیبہ کی تشریعیاتی حیثیت کا تحقیقی و تاریخی مطالعہ A Research review of the legislative Status of the Sirah of Holy Prophet ﷺ

The deeds and actions of the Holy Prophet ﷺ is a practical interpretation of the Holy Qur'an. Allah Almighty has stated his Shari'ah in short but comprehensive words that “take what the Holy Prophet ﷺ has given you and which you forbid”. Therefore, his command is the command of Allah Almighty and not yours. That is why the Holy Qur'an has declared his obedience to be the obedience of Allah Almighty. And from the instructions of the selected scholars of the ummah, it is clear that the legislative status of The Sirah is authenticated. Allah Almighty has given him a special position of Shariah. As for the legislative status of Sira-e-Taiba, both the special and the general legislation are specific or special to him. Special legislation etc i.e. you have legislated for a specific person at certain times like accepting the condition of a person that he will convert to Islam if he prays only two prayers. The other person cannot join. The general legislation in which you have enacted all kinds of legislation for the common ummah is included. The center and axis of Islamic law is your caste. You have complete control over the status and sanctity, likes and dislikes as you like, for whom you can make Shariah whenever you want. It is as if you are a follower of the Shari'ah. He also has full authority in the brief description of the Qur'an, in the adherence to the Absolute, and the explanation of similarities. You are authorized to legislate as a Shari'ah and Shari'ah in all areas of worship, affairs, debates, and crimes. Therefore, in this article, the legislative status of His Sira-e-Taiba has been explained in the light of Qur'an and Hadith, Sahabah and Tabi'een, and the commandments of Imams and jurists.

Human Action Recognition and Localization in Videos

Human action localization and recognition in videos is one of the most studied and active research area in computer vision. In this thesis we elaborate two main questions; First when and where is the action performed in the video and what type of action is performed. When and where localize the action spatially in a time series visual data, while what type of action determine the action category/class. The output of action localization is a sub-volume consists of the action of interest. Action localization is more challenging as compared to the action classi cation, because it is the process of extracting a speci c part of the spatio-temporal volume in a visual data. We address the problem of automatic extraction of foreground objects in videos and then determine the category of action performed in the localized region. Action localization and recognition deal with understanding when, where and what happens in a video sequence. In the last decade, some of the proposed methods addressed the problem of simultaneous recognition and localization of actions. Action recognition addresses the questionWhat type of action is performed in the video? , while action localization aims to answer the questionWhere in the video? . These methods are termedaction detectionoraction localization and recognition . The human action recognition and localization is greatly motivated by the wide range of applications in variouselds of computer vision like human perceptual segmentation, tracking human in a video sequence, recovering the body structure, medical diagnosis, monitoring the human activities in security-sensitive areas like airports, buildings (universities, hospitals, schools), border crossings and elderly daily activity recognition (related to elderly health issues). It is one of the hardest problems due to enormous variations in visual data, appearance of actors, motion patterns, changes in camera viewpoints, illumination variations, moving and cluttered backgrounds, occlusions of actors, intra-and inter-class variations, noise, moving cameras and the availability of extensive amount of visual data. Local features based action recognition methods have been extensively studied in the last two decades. These systems have numerous limitations and far enough from the real time scenario. Every phase of the system has its own importance for the next phase, such as the success and accuracy of local feature based methods depend on the accurate encoding of visual data i.e. feature extraction method, dimensionality reduction of the extracted features and compact representation, localizing the action and training a learning model (classi er) for the classi cation of action sequences (Main parts of the system should be: (1) Feature extraction, (2) Feature representation, (3) localization of the region of interest, and (4) classi cation of the action video). First of all we study, investigate, evaluate and compare the well known state-of-the-art and prominent approaches proposed for action recognition and localization. The methods proposed for action localization are too complex and computationally expensive. We have proposed a novel saliency map computation based on local and global features toll the gap between the two types of features and hence provide promising results very e ciently for salient object detection. Then the motion features are fused intelligently with the detected salient object to extract the moving object in each frame of the sequence. The object proposal algorithms normally use computationally expensive segmentation methodologies to extract di erent non-overlapping objects/regions in a frame. Our proposed methods exploit very limited spatio-temporal neighborhood to extract a compact action region based on the compensated motion information. Finally, classi er is trained on the local features to recognize/label the action sequence. We have evaluated two types of learning models, extreme learning machine (ELM) and deep neural networks (DNNs). ELM is fast, while the computationally intensive classi ers such as Deep Neural Networks (DNNs) produce comparatively better action recognition accuracy. The experimental evaluation reveals that our local features based human action recognition and localization system improves the existing systems in many aspects such as computational complexity and performance. Finally it is concluded that the proposed algorithms obtain better or very similar action recognition and localization performance/accuracy as compared to the state-of-the-art approaches on realistic, unconstrained and challenging human action recognition and localization datasets such as KTH, MSR-II, JHMDB21 and UCF Sports. Besides, to evaluate the e ectiveness of localization of proposed algorithms a number of segmentation data sets have been used such as MOViCs, I2R, SegTrack v 1 & 2, ObMiC and Wall owers. Though the approaches proposed in the thesis obtain promising and impressive results as compared to the prominent state-of-the-art methods, further research and investigations are required to get enhanced or comparable results on more challenging realistic videos encountered in practical life. The future directions are discussed in conclusions and future work section of the thesis.