scholarly journals A unifying model to predict multiple object orienting behaviors in tethered flies

2018 ◽  
Author(s):  
Andreas Poehlmann ◽  
Sayan J. Soselisa ◽  
Lisa M. Fenk ◽  
Andrew D. Straw

AbstractIndividual visual processing circuits for Drosophila locomotor control have been studied in detail, but contributions of specific pathways to multiple behaviors remain unclear. To address how both flexible and stereotyped visual object response behaviors potentially share neural circuit components, we investigated models of asymmetric motion responses. Such models have predicted that object fixation without explicit neural encoding of position is possible. Here we investigated what neural circuits and behaviors are consistent with such models. In behavioral experiments on tethered flying flies, we found close correspondence between T4/T5-neuron dependent turning responses to objects and model output for high frequency perturbations. Furthermore, we found that the model predicts key results from several published accounts of stereotyped object tracking. The concurrence of experiment and theory suggests a neural substrate and algorithmic basis for stereotyped object tracking and informs future studies of flexible visual behaviors and their neural bases.

Author(s):  
Daniel Tomsic ◽  
Julieta Sztarker

Decapod crustaceans, in particular semiterrestrial crabs, are highly visual animals that greatly rely on visual information. Their responsiveness to visual moving stimuli, with behavioral displays that can be easily and reliably elicited in the laboratory, together with their sturdiness for experimental manipulation and the accessibility of their nervous system for intracellular electrophysiological recordings in the intact animal, make decapod crustaceans excellent experimental subjects for investigating the neurobiology of visually guided behaviors. Investigations of crustaceans have elucidated the general structure of their eyes and some of their specializations, the anatomical organization of the main brain areas involved in visual processing and their retinotopic mapping of visual space, and the morphology, physiology, and stimulus feature preferences of a number of well-identified classes of neurons, with emphasis on motion-sensitive elements. This anatomical and physiological knowledge, in connection with results of behavioral experiments in the laboratory and the field, are revealing the neural circuits and computations involved in important visual behaviors, as well as the substrate and mechanisms underlying visual memories in decapod crustaceans.


Author(s):  
Tianyang Xu ◽  
Zhenhua Feng ◽  
Xiao-Jun Wu ◽  
Josef Kittler

AbstractDiscriminative Correlation Filters (DCF) have been shown to achieve impressive performance in visual object tracking. However, existing DCF-based trackers rely heavily on learning regularised appearance models from invariant image feature representations. To further improve the performance of DCF in accuracy and provide a parsimonious model from the attribute perspective, we propose to gauge the relevance of multi-channel features for the purpose of channel selection. This is achieved by assessing the information conveyed by the features of each channel as a group, using an adaptive group elastic net inducing independent sparsity and temporal smoothness on the DCF solution. The robustness and stability of the learned appearance model are significantly enhanced by the proposed method as the process of channel selection performs implicit spatial regularisation. We use the augmented Lagrangian method to optimise the discriminative filters efficiently. The experimental results obtained on a number of well-known benchmarking datasets demonstrate the effectiveness and stability of the proposed method. A superior performance over the state-of-the-art trackers is achieved using less than $$10\%$$ 10 % deep feature channels.


2021 ◽  
Vol 434 ◽  
pp. 268-284
Author(s):  
Muxi Jiang ◽  
Rui Li ◽  
Qisheng Liu ◽  
Yingjing Shi ◽  
Esteban Tlelo-Cuautle

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2841
Author(s):  
Khizer Mehmood ◽  
Abdul Jalil ◽  
Ahmad Ali ◽  
Baber Khan ◽  
Maria Murad ◽  
...  

Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.


IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Ershen Wang ◽  
Donglei Wang ◽  
Yufeng Huang ◽  
Gang Tong ◽  
Song Xu ◽  
...  

2015 ◽  
Vol 10 (1) ◽  
pp. 167-188 ◽  
Author(s):  
Ahmad Ali ◽  
Abdul Jalil ◽  
Jianwei Niu ◽  
Xiaoke Zhao ◽  
Saima Rathore ◽  
...  

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