Environmental perception technology is the guarantee of the safety of driverless vehicles. At present, there are a lot of researches and reviews on environmental perception, aiming to realize unmanned driving while ensuring the safety of human life. However, the technology is facing new challenges in the new era. This review paper attempts to systematically summarize environment perception technology and discuss the new challenges currently faced. To this end, we first summarized the advantages, disadvantages and applicable occasions of several commonly used sensing methods to provide a clear selection guide. The new challenges faced by environmental perception technology are discussed from three aspects: technology, external environment and applications. Finally, the article also points out the future development trends and efforts of environmental perception technology.

Sensing system of environmental perception technologies for driverless vehicle: A review of state of the art and challenges

This paper proposes an efficient video summarization framework that will give a gist of the entire video in a few key-frames or video skims. Existing video summarization frameworks are based on algorithms that utilize computer vision low-level feature extraction or high-level domain level extraction. However, being the ultimate user of the summarized video, humans remain the most neglected aspect. Therefore, the proposed paper considers human's role in summarization and introduces human visual attention-based summarization techniques. To understand human attention behavior, we have designed and performed experiments with human participants using electroencephalogram (EEG) and eye-tracking technology. The EEG and eye-tracking data obtained from the experimentation are processed simultaneously and used to segment frames containing useful information from a considerable video volume. Thus, the frame segmentation primarily relies on the cognitive judgments of human beings. Using our approach, a video is summarized by 96.5% while maintaining higher precision and high recall factors. The comparison with the state-of-the-art techniques demonstrates that the proposed approach yields ceiling-level performance with reduced computational cost in summarising the videos.

Efficient Video Summarization Framework using EEG and Eye-tracking Signals.

It is claimed that convolutional neural networks are inspired by human vision systems. Based on the literature of development of human visual system, we know that newly born child has blurred vision initially due to rapid eye movements. This rapid eye movement is termed as Nystagmus. This paper is concerned with a novel approach to quantify the nystagmus and implementing an artificial system that can mimic the visual learning of a newly born child or person with nystagmus. To quantify the nystagmus, we have recorded 10 seconds of eye movement videos from 3 subjects and 10 trials. We estimate the eye movement frequency by tracking the eye pupil through image processing, which is then used to create a database. To simulate a suitable learning environment, we have trained our model on gradually decreasing blurriness on Dog vs Cat dataset for classification task. The novelty of the paper is in the type of training which is elicited by human visual learning system. The results show that the performance of the network improves significantly when trained iteratively with increasing level of blur.

Human Visual Learning Inspired Effective Training Methods

Human observers engage in selective information uptake when classifying visual patterns. The same is true of deep neural networks, which currently constitute the best performing artificial vision systems. Our goal is to examine the congruence, or lack thereof, in the information-gathering strategies of the two systems. We have operationalized our investigation as a character recognition task. We have used eye-tracking to assay the spatial distribution of information hotspots for humans via fixation maps and an activation mapping technique for obtaining analogous distributions for deep networks through visualization maps. Qualitative comparison between visualization maps and fixation maps reveals an interesting correlate of congruence. The deep learning model considered similar regions in character, which humans have fixated in the case of correctly classified characters. On the other hand, when the focused regions are different for humans and deep nets, the characters are typically misclassified by the latter. Hence, we propose to use the visual fixation maps obtained from the eye-tracking experiment as a supervisory input to align the model's focus on relevant character regions. We find that such supervision improves the model's performance significantly and does not require any additional parameters. This approach has the potential to find applications in diverse domains such as medical analysis and surveillance in which explainability helps to determine system fidelity.

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Understanding Character Recognition using Visual Explanations Derived from the Human Visual System and Deep Networks

This study is concerned with achieving the image compression using the concept of memorability. The authors have used memorability of an image, as a perceptual measure while image coding. In the proposed approach, a region-of-interest-based memorability preserving image compression algorithm which is accomplished via two sub-processes namely, memorability prediction and image compression is introduced. The memorability of images is predicted using convolutional neural network and restricted Boltzmann machine features. Based on these features, the memorability score of individual patches in an image is calculated and these scores are used to generate the memorability map. These memorability map values are used for optimised image compression. In order to validate the results, an eye tracking experiment with human participants is performed. The comparative analysis shows that the memorability-based compression outperforms the state-of-the-art compression techniques.

Anurika Goel

Papers

Memorability-based image compression