TITAN: Future Forecast Using Action Priors

Malla, Srikanth; Dariush, Behzad; Choi, Chiho

doi:10.1109/cvpr42600.2020.01120

Cited by 71 publications

(58 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To avoid the exclusion of an undetected agent, we could attempt to impute the missing state of the agent. One possible solution is to impute a previously observed state when the miss-detection occurs [13], [14]. This can prevent the data exclusion of the agent whose state is not available.…”

Section: Miss-detectionmentioning

confidence: 99%

“…The problem of trajectory prediction has received significant attention in recent years across various applications, Trajectory prediction Gupta et al [2] Trajectory prediction Styles et al [12] Trajectory prediction Yao et al [13] Traffic accident detection Malla et al [14] Trajectory prediction Lipton et al [30] Medical data analysis Kim et al [31] Medical data analysis Che et al [32] Medical data analysis Cao et al [33] General Tian et al [34] Traffic flow prediction Kim et al [35] Medical data analysis Luo et al [36] General Luo et al [37] General Ours Trajectory prediction such as self-driving vehicles, service robots, and advanced surveillance systems. A large body of research has addressed this problem.…”

Section: Related Workmentioning

confidence: 99%

“…Another strategy is to impute the missing data with some default values and to perform the prediction over the imputed data. For example, the last observed values [13], [14], [30], [32] or zero [30] can be used to fill in the values of missing entries. Still, the filled values may cause the system to learn from them as if they are observed values, which could lead to lower performance.…”

Section: B Handling Missing Data For Rnnmentioning

confidence: 99%

“…To utilize the above mechanism when the miss-detection occurs, i.e., the new measurement z t−1 is not available, we conventionally need to synthetically generate a measurement by imputation for the update step [13], [14]. However, updating the prior distribution with a synthetically generated measurement might have a bad influence on the estimation of hidden state x t .…”

Section: B Bayesian Filter With Miss-detectionmentioning

confidence: 99%

“…Thus, we can interpret (15) as representing p(x t |z 1:t−1 ) by s t and g c being a function that approximates the computation of the expression in (11). When miss-detection occurs and the new measurement is not available, we assume another RNN works as a function approximating (14), which directly estimates the prior distribution at time step t from the prior distribution at time step t − 1 without a new measurement:…”

Section: Two-block Model For Encoding Miss-detectionmentioning

confidence: 99%

See 4 more Smart Citations

A Two-Block RNN-Based Trajectory Prediction From Incomplete Trajectory

et al. 2021

View full text Add to dashboard Cite

Trajectory prediction has gained great attention and significant progress has been made in recent years. However, most works rely on a key assumption that each video is successfully preprocessed by detection and tracking algorithms and the complete observed trajectory is always available. However, in complex real-world environments, we often encounter miss-detection of target agents (e.g., pedestrian, vehicles) caused by the bad image conditions, such as the occlusion by other agents. In this paper, we address the problem of trajectory prediction from incomplete observed trajectory due to miss-detection, where the observed trajectory includes several missing data points. We introduce a two-block RNN model that approximates the inference steps of the Bayesian filtering framework and seeks the optimal estimation of the hidden state when miss-detection occurs. The model uses two RNNs depending on the detection result. One RNN approximates the inference step of the Bayesian filter with the new measurement when the detection succeeds, while the other does the approximation when the detection fails. Our experiments show that the proposed model improves the prediction accuracy compared to the three baseline imputation methods on publicly available datasets: ETH and UCY (9% and 7% improvement on the ADE and FDE metrics). We also show that our proposed method can achieve better prediction compared to the baselines when there is no miss-detection.

show abstract

Section: Miss-detectionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: B Handling Missing Data For Rnnmentioning

confidence: 99%

Section: B Bayesian Filter With Miss-detectionmentioning

confidence: 99%

Section: Two-block Model For Encoding Miss-detectionmentioning

confidence: 99%

See 3 more Smart Citations

A Two-Block RNN-Based Trajectory Prediction From Incomplete Trajectory

et al. 2021

View full text Add to dashboard Cite

show abstract

Semantic Synthesis of Pedestrian Locomotion

Priisalu

Păduraru

Pirinen

et al. 2021

Computer Vision – ACCV 2020

View full text Add to dashboard Cite

It is difficult to perform 3D reconstruction from on-vehicle gathered video due to the large forward motion of the vehicle. Even object detection and human sensing models perform significantly worse on onboard videos when compared to standard benchmarks because objects often appear far away from the camera compared to the standard object detection benchmarks, image quality is often decreased by motion blur and occlusions occur often. This has led to the popularisation of traffic data-specific benchmarks. Recently Light Detection And Ranging (LiDAR) sensors have become popular to directly estimate depths without the need to perform 3D reconstructions. However, LiDAR-based methods still lack in articulated human detection at a distance when compared to image-based methods. We hypothesize that benchmarks targeted at articulated human sensing from LiDAR data could bring about increased research in human sensing and prediction in traffic and could lead to improved traffic safety for pedestrians.

show abstract