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Reading is one of the main paths to acquire knowledge, either done traditionally on paper media or practiced on electronic devices. Efficiency varies when different reading patterns are involved. It is the objective of this research to classify reading patterns from fixation data using machine learning techniques in an attempt to understand and evaluate the reading and learning process. In our experiment,...
Multi-object video segmentation and multi-object tracking are very similar in the aspect that both determine the locations and maintain the identities of the objects of interest (targets) in each frame of the video. Our approach takes advantage of this fact and uses the strengths of one task to improve the accuracy of the other. In our framework, the multi-object tracking and segmentation modules...
Many state-of-the-art approaches to multi-object tracking rely on detecting them in each frame independently, grouping detections into short but reliable trajectory segments, and then further grouping them into full trajectories. This grouping typically relies on imposing local smoothness constraints but almost never on enforcing more global ones on the trajectories.,,In this paper, we propose a non-Markovian...
The optimal sub-pattern assignment (OSPA) metric is a distance between two sets of points that jointly accounts for the dissimilarity in the number of points and the values of the points in the respective sets. The OSPA metric is often used for measuring the distance between two sets of points in Euclidean space. A common example is in multi-target filtering, where the aim is to estimate the set of...
Multi-target tracking is a challenging problem in video surveillance. The paper presents a multi-target tracking algorithm based on optical flow histogram and Kalman filtering. The method we proposed can effectively improve the tracking accuracy. First, targets are detected by Gaussion mixture model in real time. Second, a trajectory is initialized when a new target appears. Meanwhile, Kalman filtering...
For underactuated overhead cranes, this paper designs a novel sliding mode control method. Specifically, similar to the idea of backstepping method, the overall dynamic system is divided into two cascaded subsystems; thus the control algorithm consists of two steps. At first, the payload swing angle is kept in a bounded area by tracking a target trajectory. Second, the positioning error is indirectly...
In this paper, we propose a CNN-based framework for online MOT. This framework utilizes the merits of single object trackers in adapting appearance models and searching for target in the next frame. Simply applying single object tracker for MOT will encounter the problem in computational efficiency and drifted results caused by occlusion. Our framework achieves computational efficiency by sharing...
Maneuvering aircraft tracking under low accuracy observations is a challenge. To solve this problem, we constructed the corresponding dynamic model for different flying states of aircraft. Then we combined the models of different flying states into single one. Since an accurate dynamic model is a strong constraint for the flying trajectory of aircraft, the trajectory should follow the dynamic characteristic...
A novel online algorithm to segment multiple objects in a video sequence is proposed in this work. We develop the collaborative detection, tracking, and segmentation (CDTS) technique to extract multiple segment tracks accurately. First, we jointly use object detector and tracker to generate multiple bounding box tracks for objects. Second, we transform each bounding box into a pixel-wise segment,...
The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine cues over a long period of time in a coherent fashion. In this paper, we present an online method that encodes long-term temporal dependencies across multiple cues. One key challenge of tracking methods is to accurately track occluded targets or those which share similar appearance properties with surrounding...
This paper presents a stochastic optimal control approach suitable for implementation on small unmanned aerial vehicles (UAV). The controller provides tracking of a target at a desired distance, keeps the target away from the sector of blind spots and prevents flying over the target to avoid the flipping images in the camera's recorded sequence. The controller is based on a two-dimensional stochastic...
We present a deep trajectory feature representation approach to aid trajectory clustering and motion pattern extraction in videos. The proposed feature representation includes the use of a neural network-based approach that uses the output of the smallest hidden layer of a trained autoencoder to encapsulate trajectory information. The trajectory features are then fed into a mean-shift clustering framework...
Nowadays the task of tracking pedestrians is often addressed within a tracking-by-detection framework, which in most cases entails that the position of each target has been detected before tracking begins. However in some cases, a pedestrian who is being tracked may be obscured by other targets or obstacles, and during this period they may change their trajectory or speed (track drift), and sometimes...
Vision systems become more and more popular to be applied in monitoring tasks such as controlling traffic flows or for security issues. The analysis of target behavior is always based on its observed trajectory, which can be acquired by tracking approaches. Although the fashion of tracking-by-detection is favored by the research community, it still faces challenges like unexpected occlusion caused...
In mobile communications, the wireless channel has been widely considered to be time-variant. To statistically model the time-variant channels, a power weighted dynamic multi-path components (MPCs) tracking algorithm is proposed in this paper, which is based on multiple-target tracking. The problem of seeking potential position is considered as a maximum a posteriori (MAP) estimation in a Markov random...
Optimal control has been a very attractive and desirable feature for many dynamic and static systems, An effective online technique for finite-horizon nonlinear control problem is offered in this paper. The idea of the proposed technique is to combine the differential State Dependent Riccati equation filter algorithm and the finite-horizon SDRE technique. Genetic algorithm is used to calculate the...
The formation control problem of a multi-agent system with target tracking and navigation is resolved by a gradient based extremum seeking control (ESC) associated with artificial potential functions methodology in this paper. It aims to maintain and achieve a stable formation for a swarm of multi-agent system, while guaranteeing tracking of a specified trajectory. By incorporation of artificial potential...
In quantum state transfer and tracking, control of quantum states are effective against decoherence caused by external environment and information processing. In this paper, utilizing model reference adaptive control theory and Lyapunov stability theorem, we derive the adaptive law for the model reference adaptive system. Then we design the Lyapunov control law by double control functions and we investigate...
To obtain a better performance of trajectory tracking for hypersonic glide vehicles (HGVs), this paper focuses on enhancing the accuracy of tracking model, an effective method with a modified tracking model is proposed. Firstly, according to the skip glide trajectory of HTV-2, a simplified trajectory model is established to describe the periodical characteristic of the trajectory. Secondly, a modified...
The adaptive iterative learning control for the variable trajectories driven by a deterministic finite state machine (DFSM) are addressed in this paper. The DFSM is repressed by a linear state equation with linear-inequalities, and a state feedback controller is designed to stabilize the DFSM at an equilibrium set. Then, the variable trajectories are driven by this DFSM. Combined the Backstepping...
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