Crowd detection and counting using a static and dynamic platform: state of the art

Automated object detection and crowd density estimation are popular and important area in visual surveillance research. The last decades witnessed many significant research in this field however, it is still a challenging problem for automatic visual surveillance. The ever increase in research of the field of crowd dynamics and crowd motion necessitates a detailed and updated survey of different techniques and trends in this field. This paper presents a survey on crowd detection and crowd density estimation from moving platform and surveys the different methods employed for this purpose. This review category and delineates several detections and counting estimation methods that have been applied for the examination of scenes from static and moving platforms

Moving Object Detection for Video Surveillance

The emergence of video surveillance is the most promising solution for people living independently in their home. Recently several contributions for video surveillance have been proposed. However, a robust video surveillance algorithm is still a challenging task because of illumination changes, rapid variations in target appearance, similar nontarget objects in background, and occlusions. In this paper, a novel approach of object detection for video surveillance is presented. The proposed algorithm consists of various steps including video compression, object detection, and object localization. In video compression, the input video frames are compressed with the help of two-dimensional discrete cosine transform (2D DCT) to achieve less storage requirements. In object detection, key feature points are detected by computing the statistical correlation and the matching feature points are classified into foreground and background based on the Bayesian rule. Finally, the foreground feature points are localized in successive video frames by embedding the maximum likelihood feature points over the input video frames. Various frame based surveillance metrics are employed to evaluate the proposed approach. Experimental results and comparative study clearly depict the effectiveness of the proposed approach

PeopleNet: A Novel People Counting Framework for Head-Mounted Moving Camera Videos

Traditional crowd counting (optical flow or feature matching) techniques have been upgraded to deep learning (DL) models due to their lack of automatic feature extraction and low-precision outcomes. Most of these models were tested on surveillance scene crowd datasets captured by stationary shooting equipment. It is very challenging to perform people counting from the videos shot with a head-mounted moving camera; this is mainly due to mixing the temporal information of the moving crowd with the induced camera motion. This study proposed a transfer learning-based PeopleNet model to tackle this significant problem. For this, we have made some significant changes to the standard VGG16 model, by disabling top convolutional blocks and replacing its standard fully connected layers with some new fully connected and dense layers. The strong transfer learning capability of the VGG16 network yields in-depth insights of the PeopleNet into the good quality of density maps resulting in highly accurate crowd estimation. The performance of the proposed model has been tested over a self-generated image database prepared from moving camera video clips, as there is no public and benchmark dataset for this work. The proposed framework has given promising results on various crowd categories such as dense, sparse, average, etc. To ensure versatility, we have done self and cross-evaluation on various crowd counting models and datasets, which proves the importance of the PeopleNet model in adverse defense of society

An Automatic Zone Detection System for Safe Landing of UAVs

As the demand increases for the use Unmanned Aerial Vehicles (UAVs) to monitor natural disasters, protecting territories, spraying, vigilance in urban areas, etc., detecting safe landing zones becomes a new area that has gained interest. This paper presents an intelligent system for detecting regions to navigate a UAV when it requires an emergency landing due to technical causes. The proposed system explores the fact that safe regions in images have flat surfaces, which are extracted using the Gabor Transform. This results in images of different orientations. The proposed system then performs histogram operations on different Gabor-oriented images to select pixels that contribute to the highest peak, as Candidate Pixels (CP), for the respective Gabor-oriented images. Next, to group candidate pixels as one region, we explore Markov Chain Codes (MCCs), which estimate the probability of pixels being classified as candidates with neighboring pixels. This process results in Candidate Regions (CRs) detection. For each image of the respective Gabor orientation, including CRs, the proposed system finds a candidate region that has the highest area and considers it as a reference. We then estimate the degree of similarity between the reference CR with corresponding CRs in the respective Gabor-oriented images using a Chi square distance measure. Furthermore, the proposed system chooses the CR which gives the highest similarity to the reference CR to fuse with that reference, which results in the establishment of safe landing zones for the UAV. Experimental results on images from different situations for safe landing detection show that the proposed system outperforms the existing systems. Furthermore, experimental results on relative success rates for different emergency conditions of UAVs show that the proposed intelligent system is effective and useful compared to the existing UAV safe landing systems

ビデオからの移動物体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381号 学位授与年月日:平成27年3月25日1 Introduction||2 Moving Object Detection Based on the Update of a Background Model||3 Moving Object Detection Employing a Moving Camera||4 Performance of the Method||5 Conclusion||In recent years, the video surveillance system for security and a driving safety system on the car have been growing rapidly. The video surveillance system has grown from a manual system to a fully autonomous system, whereas a driving safety system has evolved from a parking safety system to a collision avoidance system. The system requires a good ability to detect a moving object so as to be a reliable system. The problem that must be addressed in the detection of moving objects on a video is a dynamic background. In this thesis, we proposed a moving object detection method using sequential inference of the background to overcome the problem of the dynamic background. The sequential inference of the background uses a series of previous image frames to create a model of the background image for the current frame. After a background model is obtained, then the background subtraction can be done. The proposed method is applied to the video captured using a static camera and a moving camera. The detection of moving objects in a video captured by a moving camera is not as easy as the case using a static camera. Correspondence of pixels in the current image frame with the pixels of the previous image frame must be known in advance. The background model is formed using a bilinear interpolation of the previous image frame. The judgment of a pixel as the background or the foreground is done by subtracting the model of the background image from the current frame. An important stage in this method is updating normal distribution of the pixels on a background model. A background model is formed based on the value of the normal distribution which is updated with each frame of a video. The originality of this thesis is to propose novel ways of updating the normal distribution to obtain an effective background model. Experiments are performed on several videos. The results show that the proposed method can detect and extract moving objects that appear in a video scene successfully under various situations of the background. The effectiveness of the proposed method is recognized by recall, precision and F measure