Nighttime Video Research Articles

Shadow Detection Based on Regions of Light Sources for Object Extraction in Nighttime Video.

Intelligent video surveillance systems detect pre-configured surveillance events through background modeling, foreground and object extraction, object tracking, and event detection. Shadow regions inside video frames sometimes appear as foreground objects, interfere with ensuing processes, and finally degrade the event detection performance of the systems. Conventional studies have mostly used intensity, color, texture, and geometric information to perform shadow detection in daytime video, but these methods lack the capability of removing shadows in nighttime video. In this paper, a novel shadow detection algorithm for nighttime video is proposed; this algorithm partitions each foreground object based on the object’s vertical histogram and screens out shadow objects by validating their orientations heading toward regions of light sources. From the experimental results, it can be seen that the proposed algorithm shows more than 93.8% shadow removal and 89.9% object extraction rates for nighttime video sequences, and the algorithm outperforms conventional shadow removal algorithms designed for daytime videos.

A New Efficient Illumination Compensation Algorithm for Enhancing Nighttime Videos

Since many activities of interest often occur in the dark environment, nighttime video enhancement is important for video surveillance. In this paper, we propose a novel and effective nighttime video enhancement algorithm for video surveillance applications by using illumination compensation, which fuses video frames from high quality daytime background and low quality nighttime video. For further improving the perceptual quality of the moving objects, an algorithm based on object region ratio average is also proposed. Experimental results show that the proposed algorithm can obviously improve the visual quality from the conventional methods.

FACE: Fully Automated Context Enhancement for night-time video sequences

Unfavorable lighting conditions pose significant challenges to imaging devices. Use of multiple sensor fusion and false colorization has been demonstrated by many recent works. However, semi-automatic nature of these approaches make them less attractive. To address these shortcomings, we present a color night vision system, named FACE (Fully Automated Context Enhancement). It uses enhanced multi-spectral video fusion and fully automated color transform based color morphing to process multiple video streams captured by infrared (IR) and low light visible (VIS) sensors. At first, we introduce glare-suppressed inter-channel fusion in RGB color space to negate the ghost-like effect produced by IR channel. We then introduce color value imputation with deep KNN framework for joint classification of grayscale and color imagery. A suitable color transform is then sought to give a day-like color appearance to night vision imagery. Objective quality evaluation indicates the effectiveness of our framework for context enhancement at nighttime.

Sparse codes fusion for context enhancement of night video surveillance

Fusion-based method for video enhancement has been playing a basic but significant role, which is also proved high-efficiency. Still, there are some open questions, such as lamp-off problem, over-enhanced moving objects and night shadow. To resolve the problems, a novel method--sparse codes fusion (SCF) is proposed. With plenty of samples from daytime videos and nighttime videos of the same scene, we learn and obtain a daytime dictionary and a nighttime dictionary using the proposed mutual coherence learning (MCL) algorithm. These two dictionaries are utilized for fusion and extracting context enhanced background. Moreover, we reconstruct the nighttime dictionary to get nighttime background that would be applied in motion extraction. Then the moving objects are added into the enhanced background. Extensive experimental results show a highly comprehensive description of video frames that leads to improvements over the state of the art on many usual public video datasets.

Robust vehicle tracking algorithm for nighttime videos captured by fixed cameras in highly reflective environments

In this study, the problem of vehicle detection, tracking and speed estimation in the nighttime traffic surveillance videos captured in highly reflective environments is considered. In this case, a robust algorithm is proposed which uses vehicle headlights as their prominent features. The proposed algorithm consists of three main stages. In the first stage, bright objects are segmented by thresholding the grey‐scale image. An effective algorithm is then applied to distinguish between vehicles lights and lights reflected on the road and on the vehicles bodies. In the second stage, the segmented bright objects are tracked using their spatial characteristics and their shapes and then, their speeds are estimated. To correct the camera perspective effect and reduce computational complexity, a projective transformation is used. In the third stage, the lights of each vehicle are grouped and paired using their positions and speeds. Motorbikes are also identified among the unpaired lights in this stage. Finally, the proposed real‐time system is implemented in C and applied to videos captured by traffic surveillance cameras in some highways in Iran. Experimental results reveal that accuracy of the algorithm proposed for vehicle detection is more than 98%.

Gradient-Mapping-Based Method for Video Enhancement

In order to improve the visual effect of nighttime videos, an effective video enhancement method based on gradient mapping is proposed. The proposed method firstly is to change color space from RGB to HSI, secondly the horizontal gradient and vertical gradient components of frames are calculated in I component. Then frame’s gradient is enhanced by the proposed global mapping method. Meanwhile, saturation S component is enhanced to reduce the color distortion. Finally, color video frames are reconstructed. Experimental results show that the proposed method can obtain more appealing perceptual quality than the state-of-the-art algorithms.

Multi-features-based license Plate Detection in Nighttime Environment

We propose a novel method for license plate detection in nighttime videos with complicated background scenes. The proposed method use multi-features-based combination way in the residual edge image. Our work firstly is color space conversion from RGB to La*b* color space and get enhancement L component of license plate image, then extract out multi-features of license plate, thirdly, multi-features combination method is used to get license plate detection, finally, search the license plate localization and segment the characters from the original nighttime environment images by using nonlinear vector quantization method. The experimental results show that the proposed method has more robust to interference characters and more accurate when compared with other methods.

Gradient Fusion Method for Night Video Enhancement

To resolve video enhancement problems, a novel method of gradient domain fusion wherein gradient domain frames of the background in daytime video are fused with nighttime video frames is proposed. To verify the superiority of the proposed method, it is compared to conventional techniques. The implemented output of our method is shown to offer enhanced visual quality.

Illumination-based Nighttime Video Enhancement Using Wavelet Transform

In this paper, we propose a novel approach to improve the visual appearance of nighttime video. The proposed method firstly converts the video from RGB color space to YCbCr color space, and then Y component is extracted to enhance illumination using a new image fusion method, which uses wavelet transform. Secondly, low-pass Gaussian filter is used to resolve vague areas problems of the enhanced background and moving regions. Last, the enhanced frames are reconstructed to RGB color space by combining the enhanced intensity component and corresponding color component. The experimental results demonstrate that the proposed approach is effective and competitive.

AN INDIVIDUALIZED AND COMPREHENSIVE APPROACH TO TREATING SLEEP PROBLEMS IN YOUNG CHILDREN

We evaluated the effects of assessment-based interventions on the treatment of sleep problems in 3 young children, 2 of whom had been diagnosed with autism. We used sleep diaries and infrared nighttime video in the child's bedroom to obtain measures of sleep onset, sleep-interfering behaviors, night waking, total sleep, parental presence, and medication administration each night. We then identified environmental factors that contributed to sleep problems using an open-ended interview called the Sleep Assessment and Treatment Tool. Individualized treatment packages were designed with the children's parents based on the idiosyncratic results of the assessment. Treatment packages included adjustment of the sleep schedule based on developmental norms and current sleep phases, design of a sleep-conducive environment, elimination of inappropriate sleep dependencies, and function-based interventions to decrease sleep-interfering behaviors by disrupting the contingency between the interfering behavior and its likely reinforcement. A nonconcurrent multiple baseline design across subjects revealed that treatment was effective for all 3 children. In addition, social acceptability measures showed that the parents were satisfied with the assessment process, the treatment, and the amount of behavior change.

Illumination-based nighttime video contrast enhancement using genetic algorithm

Contrast enhancement is crucial to the domain of security and surveillance where limitations in dynamic range and lack of lighting sources prevent fine details of the scene from being captured. Here, we propose a method of nighttime video contrast enhancement based on genetic algorithms. Conversion from RGB to HSI and illumination component extraction were done firstly. Illumination-based enhancement which combines chromosome, corresponding operators and genetic algorithm was then applied to enhance the contrast and details of the video according to an objective fitness criterion. Image reconstruction followed previous procedures finally. Comparison of our proposed method with other automatic enhancement techniques such as histogram equalization shows that our method produces natural looking images/videos, especially when the dynamic range of the input image is high. Results obtained, both in terms of subjective and objective evaluation, show the superiority of the proposed method.

Evaluation of Thermal Image Video Sensors for Stop Bar Detection at Signalized Intersections

Thermal image sensors for stop bar presence detection have recently been introduced to the traffic industry as an alternative to cameras sensitive only to the visual spectrum. This new detection technology, from two manufacturers, was evaluated side by side with a video detection system. Inductive loops were used for a comparison to identify discrepancies that warranted the manual establishment of ground truth of the video images. The video camera and two thermal sensors operated simultaneously over a 24-h period, and discrepancies in the loop versus thermal or video detection calls were validated from recorded video. No missed call events longer than 10 s were observed, and only a modest number of false calls were made by the test systems. The bias in activation and termination times was also evaluated during day and night operation for each system. The study found that the median time difference in activating a detection zone was about 1 s when the day and night operations of the detection system were compared with a video camera. This finding is consistent with past studies that reported nighttime detection challenges related to headlight projections. However, the thermal cameras had virtually no change in median activation times when day and nighttime operations were compared. This encouraging finding suggests that integrating cameras sensitive to the infrared spectrum holds considerable promise for improving the quality of nighttime video detection.

Intelligent Highway Traffic Surveillance With Self-Diagnosis Abilities

In this paper, we propose a self-diagnosing intelligent highway surveillance system and design effective solutions for both daytime and nighttime traffic surveillance. For daytime surveillance, vehicles are detected via background modeling. For nighttime videos, headlights of vehicles need to be located and paired for vehicle detection. An algorithm based on likelihood computation is developed to pair the headlights of vehicles at night. Moreover, to balance between the robustness and abundance of acquired information, the proposed system adapts different strategies under different traffic conditions. Performing tracking would be preferred when traffic is smooth. However, under congestion conditions, it is better to obtain traffic parameters by estimation. We utilize a time-varying adaptive system state transition matrix in Kalman filter for better prediction in a traffic surveillance scene when performing tracking. We also propose a mechanism for estimating the traffic flow parameter via regression analysis. The experimental results have shown that the self-diagnosis ability and the modules designed for the system make the proposed system robust and reliable.

Global motion estimation–based method for nighttime video enhancement

In order to efficiently enhance the dark nighttime videos, the high-quality daytime information of the same scene is often introduced to help the enhancement. However, due to camera motion, the introduced daytime may not have exactly the same scene of the nighttime videos. Thus, the final fused moving objects may not produce reasonable results. In this paper, we make the following two contributions: 1. we propose a global motion estimation-based scheme to address the problem of scene differences between daytime and nighttime videos. 2. Based on this, we further propose an improved framework for nighttime video enhancement which can efficiently recover the unreasonable enhancement results due to scene differences. Experimental results show the effectiveness of the proposed algorithm.

Image-based fusion for video enhancement of night-time surveillance

In this paper, a novel image-based fusion video enhancement algorithm is proposed for night-time video surveillance applications by a combination of illumination fusion and based on moving objects fusion. The proposed algorithm fuses video frames from high quality day-time and night-time background with low quality night-time videos. For improving the perception quality of the moving objects, based on moving objects of region fusion method is proposed. Experimental results show the effectiveness of the proposed algorithm.

Real-Time Illegal Parking Detection in Outdoor Environments Using 1-D Transformation

With decreasing costs of high-quality surveillance systems, human activity detection and tracking has become increasingly practical. Accordingly, automated systems have been designed for numerous detection tasks, but the task of detecting illegally parked vehicles has been left largely to the human operators of surveillance systems. We propose a methodology for detecting this event in real time by applying a novel image projection that reduces the dimensionality of the data and, thus, reduces the computational complexity of the segmentation and tracking processes. After event detection, we invert the transformation to recover the original appearance of the vehicle and to allow for further processing that may require 2-D data. We evaluate the performance of our algorithm using the i-LIDS vehicle detection challenge datasets as well as videos we have taken ourselves. These videos test the algorithm in a variety of outdoor conditions, including nighttime video and instances of sudden changes in weather.

Combining scene model and fusion for night video enhancement

This paper presents a video context enhancement method for night surveillance. The basic idea is to extract and fuse the meaningful information of video sequence captured from a fixed camera under different illuminations. A unique characteristic of the algorithm is to separate the image context into two classes and estimate them in different ways. One class contains basic surrounding scene information and scene model, which is obtained via background modeling and object tracking in daytime video sequence. The other class is extracted from nighttime video, including frequently moving region, high illumination region and high gradient region. The scene model and pixel-wise difference method are used to segment the three regions. A shift-invariant discrete wavelet based image fusion technique is used to integral all those context information in the final result. Experiment results demonstrate that the proposed approach can provide much more details and meaningful information for nighttime video.

Rapid Eye Movement Sleep Disturbances in Huntington Disease

Sleep disorders including insomnia, movements during sleep, and daytime sleepiness are common but poorly studied in Huntington disease (HD). To evaluate the HD sleep-wake phenotype (including abnormal motor activity during sleep) in patients with various HD stages and the length of CAG repeats. Because a mild hypocretin deficiency has been found in the brains of some patients with HD (hereinafter referred to as HD patients), we also tested the HD patients for narcolepsy. Twenty-five HD patients (including 2 premanifest carriers) underwent clinical interview, nighttime video and sleep monitoring, and daytime multiple sleep latency tests. Their results were compared with those of patients with narcolepsy and control patients. The HD patients had frequent insomnia, earlier sleep onset, lower sleep efficiency, increased stage 1 sleep, delayed and shortened rapid eye movement (REM) sleep, and increased periodic leg movements. Three HD patients (12%) had REM sleep behavior disorders. No sleep abnormality correlated with CAG repeat length. Reduced REM sleep duration (but not REM sleep behavior disorders) was present in premanifest carriers and patients with very mild HD and worsened with disease severity. In contrast to narcoleptic patients, HD patients had no cataplexy, hypnagogic hallucinations, or sleep paralysis. Four HD patients had abnormally low (< 8 minutes) daytime sleep latencies, but none had multiple sleep-onset REM periods. The sleep phenotype of HD includes insomnia, advanced sleep phase, periodic leg movements, REM sleep behavior disorders, and reduced REM sleep but not narcolepsy. Reduced REM sleep may precede chorea. Mutant huntingtin may exert an effect on REM sleep and motor control during sleep.

Randomised trial of infant sleep location on the postnatal ward

Objective: To determine whether postnatal mother–infant sleep proximity affects breastfeeding initiation and infant safety. Design: Randomised non-blinded trial analysed by intention to treat. Setting: Postnatal wards of the Royal Victoria...