People counting in sports venues is emerging as a new domain in the field of video surveillance. People counting in these venues faces many key challenges, such as severe occlusions, few pixels per head, and significant variations in person's head sizes due to wide sport areas. We propose a deep model based method, which works as a head detector and takes into consideration the scale variations of heads in videos. Our method is based on the notion that head is the most visible part in the sports venues where large number of people are gathered. To cope with the problem of different scales, we generate scale aware head proposals based on scale map. Scale aware proposals are then fed to the Convolutional Neural Network (CNN) and it provides a response matrix containing the presence probabilities of people observed across scene scales. We then use non-maximal suppression to get the accurate head positions. For the performance evaluation, we carry out extensive experiments on two standard datasets and compare the results with state-of-the-art (SoA) methods. The results in terms of Average Precision (AvP), Average Recall (AvR), and Average F1-Score (AvF-Score) show that our method is better than SoA methods.

Human brain undergoes state changes during sleep which produces distinctive signal patterns when recorded by electroencephalography (EEG). Automatic identification of these stages is crucial to diagnosing and treating sleep related disorders. We propose an image processing based technique for automatic identification of sleep stages from EEG signals. We generate two dimensional image representations from the high dynamic range Fourier transform features of the one dimensional EEG signals. Using these representations, we learn a deep and dense convolutional neural network (CNN) model for prediction. The key advantage of the proposed method is its seamless use of the existing well studied and powerful deep CNN models designed for computer vision problems. Experiments on the popular Sleep-EDF database show that the proposed method significantly outperforms the compared methods for automatic sleep stage identification.

People counting in high density crowds is emerging as a new frontier in crowd video surveillance. Crowd counting in high density crowds encounters many challenges, such as severe occlusions, few pixels per head, and large variations in person's head sizes. In this paper, we propose a novel Density Independent and Scale Aware model (DISAM), which works as a head detector and takes into account the scale variations of heads in images. Our model is based on the intuition that head is the only visible part in high density crowds. In order to deal with different scales, unlike off-the-shelf Convolutional Neural Network (CNN) based object detectors which use general object proposals as inputs to CNN, we generate scale aware head proposals based on scale map. Scale aware proposals are then fed to the CNN and it renders a response matrix consisting of probabilities of heads. We then explore non-maximal suppression to get the accurate head positions. We conduct comprehensive experiments on two benchmark datasets and compare the performance with other state-of-theart methods. Our experiments show that the proposed DISAM outperforms the compared methods in both frame-level and pixel-level comparisons.

Person detection is an important problem in computer vision with many real-world applications. The detection of a person is still a challenging task due to variations in pose, occlusions and lighting conditions. The purpose of this study is to detect human heads in natural scenes acquired from a publicly available dataset of Hollywood movies. In this work, we have used state-of-the-art object detectors based on deep convolutional neural networks. These object detectors include region-based convolutional neural networks using region proposals for detections. Also, object detectors that detect objects in the single-shot by looking at the image only once for detections. We have used transfer learning for fine-tuning the network already trained on a massive amount of data. During the fine-tuning process, the models having high mean Average Precision (mAP) are used for evaluation of the test dataset. Experimental results show that Faster R-CNN [18] and SSD MultiBox [13] with VGG16 [21] perform better than YOLO [17] and also demonstrate significant improvements against several baseline approaches.

Small drones are a rising threat due to their possible misuse for illegal activities, in particular smuggling and terrorism. The project SafeShore, funded by the European Commission under the Horizon 2020 program, has launched the “drone-vs-bird detection challenge” to address one of the many technical issues arising in this context. The goal is to detect a drone appearing at some point in a video where birds may be also present: the algorithm should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds. This paper reports on the challenge proposal, evaluation, and results.

The object detection is a challenging problem in computer vision with various potential real-world applications. The objective of this study is to evaluate the deep learning based object detection techniques for detecting drones. In this paper, we have conducted experiments with different Convolutional Neural Network (CNN) based network architectures namely Zeiler and Fergus (ZF), Visual Geometry Group (VGG16) etc. Due to sparse data available for training, networks are trained with pre-trained models using transfer learning. The snapshot of trained models is saved at regular interval during training. The best models having high mean Average Precision (mAP) for each network architecture are used for evaluation on the test dataset. The experimental results show that VGG16 with Faster R-CNN perform better than other architectures on the training dataset. Visual analysis of the test dataset is also presented.