Object tracking algorithm with adaptive color space based on CamShift

Tracking of low-visibility targets in complex backgrounds is an important research field, where existing methods struggle to image low-visibility targets with irregular changes in moving direction and speed. Move contrast imaging can greatly improve the sensitivity of target tracking, which has achieved important applications in the field of X-ray imaging, including high-resolution imaging to microvessels in living rats with the help of contrast agents, <i>in-situ</i> dynamic observation of ion migration and redox reactions during electrochemical reactions, and water refilling along vessels in willow branch without resorting to agents. However, all these applications are limited to imaging with fixed trajectories or monotonous backgrounds. In principle, move contrast imaging is based on the frequency spectral characteristics of the time-domain grayscale signal and is highly sensitive to moving components, which is wavelength-independent. This paper extends the move contrast imaging to the visible light waveband for tracking free-moving targets in time-varying complex backgrounds. To meet the need for tracking imaging of free-moving targets in complex backgrounds, we develop a move contrast imaging (MCI) method based on continuous wavelet transform (CWT) and Hilbert-Huang transform (HHT) with high discriminatory capability for non-stationary signals. Selecting birds in the sky and forest for the tracking imaging, the irregular grayscale changes caused by natural light intensity in the wild field and random swaying of tree leaves result in complex imaging backgrounds. The tracing results of low-visibility free-moving targets show that FT-MCI method, CWT-MCI method and HHT-MCI method can improve the target tracing imaging sensitivity by 179.9 times, 175.8 times and 214.6 times compared with temporal subtraction imaging, respectively. The results of tracking imaging of free-moving targets in complex backgrounds show that compared with the FT-MCI method and CWT-MCI method, the HHT-MCI method can further effectively suppress the influence of background noise on tracking imaging of targets of interest, thus achieving high sensitivity imaging of free-moving targets in time-varying complex backgrounds. Combining the phase diagram of FT-MCI and the imaging parameters, we can further show the motion direction, the motion speed or the distance from the observation point. Therefore, the HHT-MCI imaging method developed in this paper is expected to provide a novel method for tracking free-moving targets in time-varying complex backgrounds.

Visual tracking has been widely used in robot systems, and numerous approaches for visual tracking have been proposed. However, developing a robust and real-time visual tracking algorithm which can adaptively track the varying appearance of target under challenging conditions for mobile robot is still an open problem. This paper presents an adaptive probabilistic tracking algorithm with multiple cues integration. An effective evaluation function is proposed to evaluate each cue used for tracking based on their discriminating abilities between foreground and background. Then the likelihood functions of the cues are integrated in particle filter framework with different weights determined based on the evaluation scores. A novel target model updating strategy is proposed to adapt to the varying appearance of target resisting gradual drift which is still an unsolved problem in many adaptive tracking algorithms. Experimental results on a mobile robot demonstrate the robust performance of the proposed algorithm under challenging conditions.

An adaptive template matching-based single object tracking algorithm with parallel acceleration

The receiver of a global navigation satellite system (GNSS) is likely to lose tracking for a GNSS signal in some degraded environments. To solve this problem, this paper designs a new adaptive tracking algorithm for GNSS signals. First, we design an error extraction module to extract phase errors so that a CTL can estimate phase errors without using a loop discriminator, which can reduce the requirements of the carrier-to-noise ratio (CNR) of GNSS signals. Second, we design a motion detection module to detect the real-time movement status of GNSS receivers. Then, using its detecting results as inputs, we design an algorithm to automatically make the CTL switch between a second-order loop and a third-order loop. Third, we design a bandwidth-adjusting module to adjust the bandwidth of CTL, according to the CNR and the movement status of a GNSS receiver. Finally, a simulation is performed to verify that our adaptive carrier tracking algorithm can effectively improve the precision of CTL, as well as enhance its dynamic range.

The continuously adaptive mean shift (CAMshift) algorithm, an adaptive tracking model based on colour probability distribution, is effective for coping with simple cases. However, it tends to fail in the environment of a coal mine, in which complex backgrounds and low contrasts occur. This paper proposes an improved CAMshift model which generating a linear combination of the hue and saturation component to obtain state-of-the-art tracking results regardless of non-uniform illumination, similar color interference and low-level distinctiveness. In addition, speeded up robust features (SURF) is incorporated to relocate the search window when tracking is lost. The proposed object-tracking algorithm using SURF and improved CAMshift model is named as SI-CAMshift. Rigorous experiments are conducted to demonstrate the effectiveness of the object-tracking algorithm both in stability and repeatability. Moreover, real-time tracking is a further advantage of the model.

High tracking frame rates have been achieved based on traditional tracking methods which however would fail due to drifts of the object template or model, especially when the object disappears from the camera’s field of view. To deal with it, tracking-and-detection-combination has become more and more popular for long-term unknown object tracking, whose detector almost does not drift and can regain the disappeared object when it comes back. However, for online machine learning and multiscale object detection, expensive computing resources and time are required. So it is not a good idea to combine tracking and detection sequentially like Tracking-Learning-Detection algorithm. Inspired from parallel tracking and mapping, this article proposes a framework of parallel tracking and detection for unknown object tracking. The object tracking algorithm is split into two separate tasks—tracking and detection which can be processed in two different threads, respectively. One thread is used to deal with the tracking between consecutive frames with a high processing speed. The other thread runs online learning algorithms to construct a discriminative model for object detection. Using our proposed framework, high tracking frame rates and the ability of correcting and recovering the failed tracker can be combined effectively. Furthermore, our framework provides open interfaces to integrate state-of-the-art object tracking and detection algorithms. We carry out an evaluation of several popular tracking and detection algorithms using the proposed framework. The experimental results show that different tracking and detection algorithms can be integrated and compared effectively by our proposed framework, and robust and fast long-term object tracking can be realized.

By combining the classic object detection and tracking algorithms, this paper proposed an automatic detection and tracking algorithm on moving object. The Gaussian mixture model (GMM) is applied to detect object, and the fusion algorithm of Kalman filter and Camshift algorithm is utilized to track object. The Pan/Tile/Zoom (PTZ) control algorithm is used to adjust the PTZ Camera parameters, such as camera rotate and Zoom, which can make the object to locate in the centre of field. The effective of algorithm proposed was verified by hardware experiment platform. The experiment results show that the system designed can automatically detect and track moving object, overcoming the limit of camera view and expanding the scope of tracking to camera. Real-time and accuracy of the system has also been validated.

Intelligent crop detection and counting systems are critical for enhancing agricultural productivity and reducing labor costs. This paper proposes a high-precision system combining an optimized YOLOv3 model with an adaptive counting algorithm to address the limitations of traditional methods, such as low efficiency and environmental sensitivity. The YOLOv3 architecture is enhanced through multi-scale prediction and feature pyramid networks (FPN), enabling robust detection of small crop targets in complex backgrounds. Experimental results demonstrate a detection accuracy exceeding 95% and a counting error rate below 5%, outperforming conventional approaches. This system provides a scalable solution for precision agriculture, with significant theoretical and practical implications.

The infrared (IR) small target detection algorithm with a high detection rate, low false alarm rate, and high real-time performance has significant application value in the field of IR remote sensing. IR small targets in complex backgrounds have low contrast and low signal-to-noise ratio (SNR). Therefore, small target detection is more difficult. Traditional IR small target detection is generally implemented by local contrast methods (LCM), nonlocal autocorrelation methods (NAM), and adaptive segmentation. In this letter, a robust infrared small target detection network (RISTDnet) is proposed based on deep learning. In RISTDnet, a feature extraction framework combining handcrafted feature methods and convolutional neural networks is constructed, a mapping network between feature maps and the likelihood of small targets in the image is established, and a threshold is applied on the likelihood map to segment real targets. Experimental results show that the RISTDnet can detect small targets with different sizes and low SNRs in complex backgrounds and have better effectiveness and robustness against existing algorithms.

In order to solve the problem of high false alarm rate caused by strong disturbance (the ground objects, uncertain strong noise) in complex background, we propose a novel algorithm based on density peaks searching and improved region growing. Initially, density peaks searching is employed to select the candidate targets quickly; then, improved region growing with introducing the relative local contrast and the local gray average is used to extract the features of candidate targets; finally, an adaptive threshold is applied to extract real targets. Experimental results on two real sequences show that the proposed algorithm has a better effectiveness against existing algorithms.

In this paper an advanced anti-jam indoor adaptive GNSS signal acquisition and tracking algorithm is considered. Initially, we were able to determine that double-dwell structure (DDS) reduces the processing time penalty caused by false alarm. Nevertheless, the double-dwell structure is still vulnerable to interference and jamming. In order to determine a suitable advanced anti-jam indoor adaptive GNSS signal acquisition and tracking algorithm for DDS we first perform the Bayesian parameter estimation; i.e., we analytically compute the posterior Bayes probability density function (pdf) and cumulative distribution function (cdf) by applying the Bayes theorem in three steps. First, we compute the complex signal distribution and complex matrix variate signal distribution. This is an original new result never published before. Second, we provide an introduction of the complex Bessel or parabolic function interference distribution due to earlier assumptions of the DDS interference distribution models. We further explain that the process that is required to produce Complex Matrix Variate Bessel Interference Distribution or Complex Matrix Variate Parabolic Function Interference Distribution is an extremely daunting task let alone the Complex Matrix Variate DDS Bayesian posterior density it would seem at the moment nearly an impossible task because it may require the computation of functions such as Kampe de Feriet function and Jack functions of matrix arguments. Third, instead of performing the second step, we relax the assumption of interference to normal distribution. In both the scalar case and complex matrix variate cases we observe that the complex matrix variate Bayesian posterior pdf or cdf is invariant of the observation data or is identical to the prior complex matrix variate signal distribution model. This is an original new and very powerful result never published before. Why this result is so powerful is because up until now we never had a complete theoretical validation of our GNSS receiver design based on either autocorrelation or cross-correlation properties since, the complex matrix variate Bayesian posterior pdf or cdf is invariant of the observation data or is identical to the prior complex matrix variate signal distribution model. Future simulation results will illustrate that an anti-jam indoor adaptive DDS stable detection structure reduces by half the average acquisition time and significantly outperform its predecessor against interference and jamming. Moreover, future simulation results will show further advancements of the Giftet Inc. MATLAB library capability to perform advanced numerical computations based on closed form expressions of the generalized Bessel function distributions and generalized parabolic cylinder function distribution models via Kampe de Feriet function and Jack functions.

At endmost stage of flight for tracking, target often tends to fulfill whole scene. If the target maneuvers rapidly, it is easy to lose the target. In this paper, an adaptive template update tracking algorithm model based on graph-regularized salience is proposed to solve this problem. The proposed algorithm introduces the graph-regularized salience model into the target tracking framework. When the target’s size exceeds the setting threshold or the target will overflow the screen in the setting number of frames, that is, the target’s lifetime, salience model is used to extract the most prominent position of the target to update the template, in order to ensure that the template has a good response to the target and ensure the stability of target tracking.

A review of object tracking methods: From general field to autonomous vehicles

Event cameras utilize novel imaging sensors patterned after visual pathways in the brain that are responsive to low contrast, transient events. Specifically, the pixels of dynamic vision sensors (DVS) react independently and asynchronously to changes in light intensity, creating a stream of time-stamped events encoding the pixels’ (x, y) location in the sensor array and the sign of the brightness change. In contrast with conventional cameras that sample every pixel at a fixed rate, DVS pixels produce output only when the change in intensity has surpassed a set threshold, which leads to reduced power consumption in scenes with relatively little motion. Furthermore, compared to conventional CMOS imaging pixels, DVS pixels have extremely high dynamic range, low latency, and low motion blur. Taken together, these characteristics make event cameras uniquely qualified for persistent surveillance. In particular, we have been investigating their use in port surveillance applications. Such an application of DVS presents the need for automated pattern recognition and object tracking algorithms which can process event data. Due to the fundamentally different nature of the output relative to conventional frame-based cameras, traditional methods of machine learning for computer vision cannot be directly applied. Anticipating this need, this work details data collection and collation efforts to facilitate development of object detection and tracking algorithms in this modality. We have assembled a maritime dataset capturing several moving objects including sail boats, motor boats, large ships, etc.; as well as incidentally captured objects. The data was collected with lenses of various focal lengths and aperture settings to provide data variability and avoid unwanted bias to specific sensor parameters. In addition, the captured data was recorded with the camera in both static and dynamic states. These different states can be used to mimic potential behavior and help understand how this movement can affect the algorithms being developed for automated ship detection and tracking. We will describe the data captured, effects of hardware settings and lenses, as well as how lighting conditions and sensor movement contributed to the quality of the event data recorded. Finally, we will detail future data collection efforts.

This paper presents a novel real-time scale adaptive visual tracking framework and its use in smart traffic monitoring where the framework robustly detects and tracks vehicles from a stationary camera. Existing visual tracking methods often employ semi-supervised appearance models where a set of samples are continuously extracted around the object to train a discriminant classifier between the vehicle and the background. While proving their advantage, many issues are still to be addressed. One is the tradeoff between high adaptability (prone to drift) and preserving original vehicle appearance (susceptible to tracking loss with appearance changes). Another issue is vehicle scale variations due to perspective camera effects which increase the potential for inaccurate classifier update and subsequently tracking drift. Still, scale adaptability received little attention in discriminant trackers. In this paper we propose a Scale Adaptive Object Tracking (SAOT) algorithm that adapts to scale and appearance changes. The algorithm is divided into three phases: (1) vehicle localization using a diverse ensemble of multiple random projections, (2) scale estimation, and (3) data association where detected and tracked vehicles are correlated. Experimental results demonstrate that our method provides robust tracking in case of significant scale variations and helps alleviate the tracker drift problem.