D. Du et al. related fields. The collected dataset is formed by 3, 360 images, including 2, 460 images for training, and 900 images for testing. Specifically, we manually annotate persons with points in each video frame. There are 14 algorithms from 15 institutes submitted to the VisDrone-CC2020 Challenge. We provide a detailed analysis of the evaluation results and conclude the challenge. More information can be found at the website: http://www.aiskyeye.com/.

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The objective of this work is to characterize the porosity distribution and the types of carbonate facies in the Mishrif reservoir in the West Qurna/1 Oil Field using seismic inversion results, well log data, rock physics model and core data analysis. Identification of the pore system and the spatial distribution of lithology are keys for constructing Mishrif reservoir model, which have a great impact on the development of the most prolific reservoir in the field Mishrif reservoir. Seismic inversion process is transforming of seismic response into a quantitative elastic properties of the reservoir rocks. It enables the modeling of porosity and lithology distribution in 3D space away from well control. In order to achieve the aim of the work, a step wise approach will be taken. First of all, the vertical distribution of porosity based on well log data and its relationship with elastic properties was undertaken. A model-based seismic inversion guided by rock physic analysis modeling was applied across the high resolution 3-D seismic data, and integrated with core data for validating at Mishrif intervals. The porosity volume was then generated over the entire West Qurna/1 field based on the linear-regression analysis. The interpretation of seismically derived characterization in the Mishrif reservoir observed a different lateral distribution of acoustic impedance. The results were correlated with computed acoustic impedance log and core analysis data to classify lithofacies of the Mishrif interval. The resulting porosity volume was validated with well log data where good consistency was indicated. By slicing through the porosity volume, it shows a high porosity in many carbonate features with low acoustic impedance which reflect a good reservoir quality (grainstone tidal channel or the accumulation of corals and mounds facies). This observation implied that Mishrif zones displayed a wide range of porosity and lithology fluctuations due to the impact of depositional environment. The workflow provided insight into the porosity distribution and quantification of its influence on dynamic reservoir behavior. The estimation of the porosity based on seismic data, can increase the reliability of reservoir characteristics through providing a more detailed of porosity distribution in interwell regions. Overall, this study will ultimately lead to improve the development plan of wells in terms of production performance, and economic value of the West Qurna/1 oil field and similar heterogeneous carbonate reservoirs.

Counting the number of people in a crowd has gained attention in the last decade. Due to its benefit to many applications such as crowd behavior analysis, crowd management, and video surveillance systems, etc. Counting crowded scenes, like stadiums, represents a challenging task due to the inherent occlusions and density of the crowd inside and outside the stadiums. Finding a pattern to control thousands of people and counting them is a challenging task. With the introduction of Convolutional Neural Networks (CNN), enables performing this task with acceptable performance. The accuracy of a CNN-based method is related to the size of data used for training. The availability of the dataset is sparse. In particular, there is no dataset in the literature that can be used for training applications for crowd scene. This paper proposes two main contributions including a new dataset for crowd counting, and a CNN-based method for counting the number of people and generating the crowd density maps. The proposed dataset for Football Supporters Crowd (FSC-Set) is composed of 6000 annotated images (manually) of different types of scenes that contain thousands of people gathering in or around the stadiums. FSC-Set contains more than 1.5 Million individuals. The collected images are captured under varying Fields of Views (FOV), illuminations, resolutions, and scales. The proposed dataset can also be utilized for other applications, such as individual's localization and face detection as well as team recognition from supporter images. Further, we propose a CNN-based method named FSCNet for crowd counting exploiting context-aware attention, spatial-wise attention, and channel-wise attention modules. The proposed method is evaluated on our established FSC-Set and other existing datasets then compared to state-of-the-art methods. The obtained results show satisfactory performances on all the datasets. The dataset is made publicly available and can be requested using the following link: https://sites.google.com/view/fscrowd-dataset/ INDEX TERMS Crowd Counting, Football Supporters Crowd, Density map, Crowd management.

Facies classification is significant for characterization and evaluation of a reservoir because the distribution of facies has an important impact on reservoir modelling which is important for decision making and maximizing return. Facies classification using data from sources such as wells and outcrop cannot capture all reservoir characterization in the inter-well region and therefore as an alternative approach, seismic facies classification schemes have to be applied to reduce the uncertainties in the reservoir model. In this research, a machine learning neural network was introduced to predict the lithology required for building a full field earth model for carbonate reservoirs in Sothern Iraq. In the present research, multilayer feed forward network (MLFN) and probabilistic neural network (PNN) were undertaken to classify facies and its distribution. The well log that was used for litho-facies classification is based on a porosity log. The spatial distribution of litho-facies was validated carefully using core data. Once successfully trained, final results show that PNN technique classified the carbonate reservoir into four facies, while the MLFN presented two facies. The final results on a blind well, show that PNN technique has the best performance on facies classification. These observations implied this reservoir consists of a wide range of lithology and porotype fluctuations due to the impact of depositional environment. The work and the methodology provide a significant improvement of the facies classification and revealed the capability of probabilistic neural network technique when tested against the neural network. Therefore, it proved to be very successful as developed for facies classification in carbonate rock types in the Middle East and similar heterogeneous carbonate reservoirs.