End-to-End Learning of Object Motion Estimation from Retinal Events for Event-Based Object Tracking

Event cameras, which are asynchronous bio-inspired vision sensors, have shown great potential in computer vision and artificial intelligence. However, the application of event cameras to object-level motion estimation or tracking is still in its infancy. The main idea behind this work is to propose a novel deep neural network to learn and regress a parametric object-level motion/transform model for event-based object tracking. To achieve this goal, we propose a synchronous Time-Surface with Linear Time Decay (TSLTD) representation, which effectively encodes the spatio-temporal information of asynchronous retinal events into TSLTD frames with clear motion patterns. We feed the sequence of TSLTD frames to a novel Retinal Motion Regression Network (RMRNet) to perform an end-to-end 5-DoF object motion regression. Our method is compared with state-of-the-art object tracking methods, that are based on conventional cameras or event cameras. The experimental results show the superiority of our method in handling various challenging environments such as fast motion and low illumination conditions.

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Aberrance suppressed spatio-temporal correlation filters for visual object tracking

Pattern Recognition ◽

10.1016/j.patcog.2021.107922 ◽

2021 ◽

Vol 115 ◽

pp. 107922

Author(s):

Dinesh Elayaperumal ◽

Young Hoon Joo

Keyword(s):

Object Tracking ◽

Temporal Correlation ◽

Visual Object ◽

Correlation Filters ◽

Visual Object Tracking ◽

Spatio Temporal

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Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Sensors ◽

10.3390/s21082841 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2841

Author(s):

Khizer Mehmood ◽

Abdul Jalil ◽

Ahmad Ali ◽

Baber Khan ◽

Maria Murad ◽

...

Keyword(s):

Kalman Filter ◽

Object Tracking ◽

Environmental Changes ◽

Correlation Filter ◽

Temporal Context ◽

Visual Object ◽

Target Model ◽

Change Of Scale ◽

Tracking Model ◽

Spatio Temporal

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.

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