In this paper, we present our multidisciplinary initiative of creating a publicly available dataset to facilitate the visual-related marketing research and applications in automotive industry such as automotive exterior design, consumer analytics and sales modelling.
The developed visual system comprises three main subsystems, namely, an attention module, an STMD-based neural network, and a prediction module.
The main contributions of this research are on two aspects: 1) the proposed model articulates the forming of both direction-selective (DS) and direction-opponent (DO) responses, revealed as principal features of motion perception neural circuits, in a feed-forward manner; 2) it also shows robust direction selectivity to translating objects in front of cluttered moving backgrounds, via the modelling of spatiotemporal dynamics including combination of motion pre-filtering mechanisms and ensembles of local correlators inside both the ON and OFF pathways, which works effectively to suppress irrelevant background motion or distractors, and to improve the dynamic response.
Inspired by insects' visual brains, this paper presents original modelling of a complementary visual neuronal systems model for real-time and robust collision sensing.
The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably, and has potential to be implemented on embedded collision detection systems for small or micro UAVs.
Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro robots that are generally limited in computational power.
The presented system is a synthetic neural network, which comprises two complementary sub-systems with four spiking neurons -- the lobula giant movement detectors (LGMD1 and LGMD2) in locusts for sensing looming and recession, and the direction selective neurons (DSN-R and DSN-L) in flies for translational motion extraction.
The directional contrast and the extracted motion information by the motion pathway are integrated in the mushroom body for small target discrimination.
This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios.
To address this issue, we develop a new visual system model for small target motion detection against cluttered moving backgrounds.
With respect to biological findings underlying fly's physiology in the past decade, we present a directionally selective neural network, with a feed-forward structure and entirely low-level visual processing, so as to implement direction selective neurons in the fly's visual system, which are mainly sensitive to wide-field translational movements in four cardinal directions.
However, if there exists a feedback loop in the STMD visual pathway or if a feedback loop could significantly improve the detection performance of STMD neurons, is unclear.
The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration.
Discriminating targets moving against a cluttered background is a huge challenge, let alone detecting a target as small as one or a few pixels and tracking it in flight.
The observed results from the experiments demonstrated that the LGMD collision detector is feasible to work as a vision module for the quadcopter's collision avoidance task.
In this paper we propose a novel image feature matching algorithm, integrating our previous proposed Affine Invariant Feature Detector (AIFD) and new proposed Affine Invariant Feature Descriptor (AIFDd).
The results demonstrated this framework is able to detect looming dark objects embedded in bright backgrounds selectively, which make it ideal for ground mobile platforms.