Object Tracking by the Least Spatiotemporal Searches

Yu, Zhiyong; Han, Lei; Chen, Chao; Guo, Wenzhong; Yu, Zhiwen

doi:10.1109/jiot.2021.3063779

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…The mobility prediction algorithm is not the focus of this paper. In the previous work [5], we have analyzed the advantages of choosing the first-order Markov model for mobility prediction. Suppose that the transition probability matrix TPM jLj×jLj Δt represents the probabilities of a vehicle moving from one location to another after a period of time Δt = t opt − t s , of which an element is p opt l i , i.e., the probability of vehicle o x moves from location l s at moment t s to location l i at moment t opt .…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we will describe three spatiotemporal searching algorithms as baselines: ALT, IEM, and IHMs [5]. Given the location l s of the vehicle o x at moment t s of d j , the process of the three algorithms are as follows:…”

Section: Baselinesmentioning

confidence: 99%

“…The cost of a spatiotemporal search can be considered as a constant for simplicity, regarding either human or artificial intelligence. In previous work [5], we proposed two greedy algorithms named Intermediate Searching at Heuristic Spatiotemporal Units (IHUs) and Intermediate Searching at Heuristic Moments (IHMs), and some other simple but uncompetitive baselines. Compared with the algorithm IHUs that considers both moments and location, IHMs can fully utilize the information gained from previous failed searches, performing location-bylocation searches at the same time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Minimizing the Cost of Spatiotemporal Searches Based on Reinforcement Learning with Probabilistic States

Han

et al. 2022

Wireless Communications and Mobile Computing

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Portraying the trajectories of certain vehicles effectively is of great significance for urban public safety. Specially, we aim to determine the location of a vehicle at a specific past moment. In some situations, the waypoints of the vehicle’s trajectory are not directly available, but the vehicle’s image may be contained in massive camera video records. Since these records are only indexed by location and moment, rather than by contents such as license plate numbers, finding the vehicle from these records is a time-consuming task. To minimize the cost of spatiotemporal search (a spatiotemporal search means the effort to check whether the vehicle appears at a specified location at a specified moment), this paper proposes a reinforcement learning algorithm called Quasi-Dynamic Programming (QDP), which is an improved Q-learning. QDP selects the searching moment iteratively based on known past locations, considering both the cost efficiency of the current action and its potential impact on subsequent actions. Unlike traditional Q-learning, QDP has probabilistic states during training. To address the problem of probabilistic states, we make the following contributions: 1) replaces the next state by multiple states of a probability distribution; 2) estimates the expected cost of subsequent actions to calculate the value function; 3) creates a state and an action randomly in each loop to train the value function progressively. Finally, experiments are conducted using real-world vehicle trajectories, and the results show that the proposed QDP is superior to the previous greedy-based algorithms and other baselines.

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Section: Related Workmentioning

confidence: 99%

Section: Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Minimizing the Cost of Spatiotemporal Searches Based on Reinforcement Learning with Probabilistic States

Han

et al. 2022

Wireless Communications and Mobile Computing

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“…Multi-object tracking (MOT) is a key component in intelligent video surveillance based on IoT devices. It can provide spatial-temporal state information to assist in decision making [8]- [14], for example, the intelligent transportation system in Figure 1. However, most existing MOT methods are difficult to deploy to IoT devices with limited computing capacity and storage.…”

Section: Introductionmentioning

confidence: 99%

Multiobject Tracking via Discriminative Embeddings for the Internet of Things

Liang

Yin

et al. 2023

IEEE Internet Things J.

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“…Several location-based service (LBS) applications (e.g., navigation [1], location tracking [2], intelligent transportation systems (ITS) [3][4][5][6], location-based mobile commerce [7], location-based emergency services [8], and location-based event recommendation [9]) have been designed and implemented based on advanced positioning techniques and mobile/cellular communication techniques. For obtaining the LBS applications, mobile devices (MDs) and LBS servers are designed in the architecture of LBS.…”

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confidence: 99%