Adaptive Fuzzy Video Compression Control for Advanced Driver Assistance Systems

Krömer, Pavel; Prauzek, Michal; Stankuš, Martin; Konecny, Jaromír

doi:10.1109/icseng.2018.8638184

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…A wide portfolio of metaheuristic techniques, including all classic evolutionary and swarm-based algorithms, altogether with the symbolic nature of some evolutionary methods (e.g., genetic programming), have been introduced for the design, learning, and optimization of FSs/FRBSs [16,15,22], thus creating evolutionary (genetic) fuzzy systems [16]. Algorithms such as ant colony optimization (ACO) [13], genetic algorithms (GA) [12], genetic programming (GP) [25,24,7], multigene genetic programming [34], artificial bee colony (ABC) optimization [6], differential evolution (DE) [36,53,71,40], especially its stateof-the-art version SHADE/L-SHADE [72], have all been used to address research tasks. Also, multi-objective optimization has become an important approach for optimal design and learning of FSs/FRBSs [58,56,2].…”

Section: Metaheuristics For Fuzzy Rulesmentioning

confidence: 99%

“…There is a broad application field of evolutionary optimized (synthesized) FSs such as fuzzy intrusion detection system [20], classification of healthcare data [44], decision support in healthcare [1], medical diagnostics [26], advanced driver assistance systems [36], linguistic modelling [15,16] or deep evolving fuzzy neural networks [52].…”

Section: Metaheuristics For Fuzzy Rulesmentioning

confidence: 99%

“…Finally, FS/FRBS optimization and design can be seen as a challenging high-dimensional optimization problem, mostly with many objectives. Since the modern DE versions have been used in the most recent frameworks (like DECO3RUM) [72,71] and single/multi-objective applications [36], the following text is focused on this powerful algorithm.…”

Section: Metaheuristics For Fuzzy Rulesmentioning

confidence: 99%

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Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Korytkowski

Šenkeřík

Scherer

et al. 2019

Journal of Artificial Intelligence and Soft Computing Research

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Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.

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Section: Metaheuristics For Fuzzy Rulesmentioning

confidence: 99%

Section: Metaheuristics For Fuzzy Rulesmentioning

confidence: 99%

Section: Metaheuristics For Fuzzy Rulesmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Korytkowski

Šenkeřík

Scherer

et al. 2019

Journal of Artificial Intelligence and Soft Computing Research

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show abstract

“…are transmitted uncompressed by using dedicated copper based cables and connectors which are expensive and heavy [7]. Therefore, the need for considering and evaluating data reduction and compression techniques arises in automotive, however a careful analysis of potential loss of key information, generation of artefacts, bit error propagation, combined with any delays introduced by the coding-decoding process is needed [8][9].…”

Section: Introductionmentioning

confidence: 99%

Influence of AVC and HEVC compression on detection of vehicles through Faster R-CNN

Chan¹,

Huggett²,

Souvalioti³

et al. 2022

Preprint

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<div><div><div><p>Situational awareness based on the data collected by the vehicle perception sensors (i.e. LiDAR, RADAR, camera and ultrasonic sensors) is key for achieving assisted and automated driving functions in a safe and reliable way. However, the data rates generated by the sensor suite are difficult to support over traditional wired data communication networks on the vehicle, hence there is an interest in techniques that reduce the amount of sensor data to be transmitted without losing key information or introducing unacceptable delays. These techniques must be analysed in combination with the consumer of the data, which will most likely be a machine learning algorithm based on deep neural networks (DNNs). In this paper we demonstrate that by compression tuning the DNNs (i.e. transfer learning by re-training with compressed data) the DNN average precision and recall can significantly improve when uncompressed and compressed data are transmitted. This improvement is achieved independently from the compression standard used for compression-training (we used AVC and HEVC), and also when training and transmitted data use the same compression standard or different compression standards. Furthermore, the performance of the DNNs is stable when transmitting data with increasing lossy compression rate, up to a compression ratio of approximately 1200:1; above this value the performance starts to degrade. This work paves the way for the use of compressed sensor data in assisted and automated driving in combination with the optimisation of compression- tuned DNNs.</p></div></div></div>

show abstract

“…are transmitted uncompressed by using dedicated copper based cables and connectors which are expensive and heavy [7]. Therefore, the need for considering and evaluating data reduction and compression techniques arises in automotive; however a careful analysis of potential loss of key information, artefacts, bit error propagation, combined with any delays introduced by the coding-decoding process, is needed [8][9].…”

mentioning

confidence: 99%