1,357 research outputs found
A flexible algorithm for detecting challenging moving objects in real-time within IR video sequences
Real-time detecting moving objects in infrared video sequences may be particularly challenging because of the characteristics of the objects, such as their size, contrast, velocity and trajectory. Many proposed algorithms achieve good performances but only in the presence of some specific kinds of objects, or by neglecting the computational time, becoming unsuitable for real-time applications. To obtain more flexibility in different situations, we developed an algorithm capable of successfully dealing with small and large objects, slow and fast objects, even if subjected to unusual movements, and poorly-contrasted objects. The algorithm is also capable to handle the contemporary presence of multiple objects within the scene and to work in real-time even using cheap hardware. The implemented strategy is based on a fast but accurate background estimation and rejection, performed pixel by pixel and updated frame by frame, which is robust to possible background intensity changes and to noise. A control routine prevents the estimation from being biased by the transit of moving objects, while two noise-adaptive thresholding stages, respectively, drive the estimation control and allow extracting moving objects after the background removal, leading to the desired detection map. For each step, attention has been paid to develop computationally light solution to achieve the real-time requirement. The algorithm has been tested on a database of infrared video sequences, obtaining promising results against different kinds of challenging moving objects and outperforming other commonly adopted solutions. Its effectiveness in terms of detection performance, flexibility and computational time make the algorithm particularly suitable for real-time applications such as intrusion monitoring, activity control and detection of approaching objects, which are fundamental task in the emerging research area of Smart City
Improving Detection of Dim Targets: Optimization of a Moment-based Detection Algorithm
Wide area motion imagery (WAMI) sensor technology is advancing rapidly. Increases in frame rates and detector array sizes have led to a dramatic increase in the volume of data that can be acquired. Without a corresponding increase in analytical manpower, much of these data remain underutilized. This creates a need for fast, automated, and robust methods for detecting dim, moving signals of interest. Current approaches fall into two categories: detect-before-track (DBT) and track-before-detect (TBD) methods. The DBT methods use thresholding to reduce the quantity of data to be processed, making real time implementation practical but at the cost of the ability to detect low signal to noise ratio (SNR) targets without acceptance of a high false alarm rate. TBD methods exploit both the temporal and spatial information simultaneously to make detection of low SNR targets possible, but at the cost of computation time. This research seeks to contribute to the near real time detection of low SNR, unresolved moving targets through an extension of earlier work on higher order moments anomaly detection, a method that exploits both spatial and temporal information but is still computationally efficient and massively parallellizable. The MBD algorithm was found to detect targets comparably with leading TBD methods in 1000th the time
Particle Filtering for Large Dimensional State Spaces with Multimodal Observation Likelihoods
We study efficient importance sampling techniques for particle filtering (PF)
when either (a) the observation likelihood (OL) is frequently multimodal or
heavy-tailed, or (b) the state space dimension is large or both. When the OL is
multimodal, but the state transition pdf (STP) is narrow enough, the optimal
importance density is usually unimodal. Under this assumption, many techniques
have been proposed. But when the STP is broad, this assumption does not hold.
We study how existing techniques can be generalized to situations where the
optimal importance density is multimodal, but is unimodal conditioned on a part
of the state vector. Sufficient conditions to test for the unimodality of this
conditional posterior are derived. The number of particles, N, to accurately
track using a PF increases with state space dimension, thus making any regular
PF impractical for large dimensional tracking problems. We propose a solution
that partially addresses this problem. An important class of large dimensional
problems with multimodal OL is tracking spatially varying physical quantities
such as temperature or pressure in a large area using a network of sensors
which may be nonlinear and/or may have non-negligible failure probabilities.Comment: To appear in IEEE Trans. Signal Processin
Small Object Detection and Tracking: A Comprehensive Review
Object detection and tracking are vital in computer vision and visual surveillance, allowing for the detection, recognition, and subsequent tracking of objects within images or video sequences. These tasks underpin surveillance systems, facilitating automatic video annotation, identification of significant events, and detection of abnormal activities. However, detecting and tracking small objects introduce significant challenges within computer vision due to their subtle appearance and limited distinguishing features, which results in a scarcity of crucial information. This deficit complicates the tracking process, often leading to diminished efficiency and accuracy. To shed light on the intricacies of small object detection and tracking, we undertook a comprehensive review of the existing methods in this area, categorizing them from various perspectives. We also presented an overview of available datasets specifically curated for small object detection and tracking, aiming to inform and benefit future research in this domain. We further delineated the most widely used evaluation metrics for assessing the performance of small object detection and tracking techniques. Finally, we examined the present challenges within this field and discussed prospective future trends. By tackling these issues and leveraging upcoming trends, we aim to push forward the boundaries in small object detection and tracking, thereby augmenting the functionality of surveillance systems and broadening their real-world applicability
Unmanned aerial vehicle video-based target tracking algorithm Using sparse representation
Target tracking based on unmanned aerial vehicle
(UAV) video is a significant technique in intelligent urban
surveillance systems for smart city applications, such as smart
transportation, road traffic monitoring, inspection of stolen
vehicle, etc. In this paper, a vision-based target tracking algorithm
aiming at locating UAV-captured targets, like pedestrian and
vehicle, is proposed using sparse representation theory. First of all,
each target candidate is sparsely represented in the subspace
spanned by a joint dictionary. Then, the sparse representation
coefficient is further constrained by an L2 regularization based on
the temporal consistency. To cope with the partial occlusion
appearing in UAV videos, a Markov Random Field (MRF)-based
binary support vector with contiguous occlusion constraint is
introduced to our sparse representation model. For long-term
tracking, the particle filter framework along with a dynamic
template update scheme is designed. Both qualitative and
quantitative experiments implemented on visible (Vis) and
infrared (IR) UAV videos prove that the presented tracker can
achieve better performances in terms of precision rate and success
rate when compared with other state-of-the-art tracker
A Comparative Evaluation of the Detection and Tracking Capability Between Novel Event-Based and Conventional Frame-Based Sensors
Traditional frame-based technology continues to suffer from motion blur, low dynamic range, speed limitations and high data storage requirements. Event-based sensors offer a potential solution to these challenges. This research centers around a comparative assessment of frame and event-based object detection and tracking. A basic frame-based algorithm is used to compare against two different event-based algorithms. First event-based pseudo-frames were parsed through standard frame-based algorithms and secondly, target tracks were constructed directly from filtered events. The findings show there is significant value in pursuing the technology further
Embedded Real Time Gesture Tracking
Video tracking is the process of locating a moving object (or several ones) in time using a camera. An algorithm evaluates the video frames and outputs the location of moving targets within the video frame
A self-selective correlation ship tracking method for smart ocean systems
In recent years, with the development of the marine industry, the ship navigation environment has become more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count sailing ships to ensure maritime security and facilitate management for Smart Ocean systems. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly includes: (1) A self-selective model with a negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of the classifier at the same time; (2) a bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were over 8 % higher than Discriminative Scale Space Tracking (DSST) on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 frames per second (FPS).This research was supported by the National Natural Science Foundation of China under Grant (No. 61772387 and No. 61802296), the Fundamental Research Funds of Ministry of Education and China Mobile (MCM20170202), the Fundamental Research Funds for the Central Universities (JB180101), China Postdoctoral Science Foundation Grant (No. 2017M620438), and supported by ISN State Key Laboratory
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