Joint image enhancement and localization framework for vehicle model recognition in the presence of non-uniform lighting conditions

Kezebou, Landry; Oludare, Victor; Panetta, Karen; Agaian, Sos С.

doi:10.1117/12.2586036

Cited by 3 publications

(2 citation statements)

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“…= Number of frames where target present but "Not localized" Total number of frames in testbench dataset "where target present" (7) where K is the total number of video sequences in the testbench, F j is the total number of frames in the jth video sequence; L(y i = z i ) is the localization function, which indicates that the target found does not match the known target; P(z i = 0 | y i = 1) is the probability that a target is not found in the ith frame of the jth video, knowing that the target is present in that frame; and ∑ F j P(y i = 1) is the total number of frames where the known target is present in a given video.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Artificial Intelligence for Text-Based Vehicle Search, Recognition, and Continuous Localization in Traffic Videos

Panetta

Kezebou

Oludare

et al. 2021

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The concept of searching and localizing vehicles from live traffic videos based on descriptive textual input has yet to be explored in the scholarly literature. Endowing Intelligent Transportation Systems (ITS) with such a capability could help solve crimes on roadways. One major impediment to the advancement of fine-grain vehicle recognition models is the lack of video testbench datasets with annotated ground truth data. Additionally, to the best of our knowledge, no metrics currently exist for evaluating the robustness and performance efficiency of a vehicle recognition model on live videos and even less so for vehicle search and localization models. In this paper, we address these challenges by proposing V-Localize, a novel artificial intelligence framework for vehicle search and continuous localization captured from live traffic videos based on input textual descriptions. An efficient hashgraph algorithm is introduced to compute valid target information from textual input. This work further introduces two novel datasets to advance AI research in these challenging areas. These datasets include (a) the most diverse and large-scale Vehicle Color Recognition (VCoR) dataset with 15 color classes—twice as many as the number of color classes in the largest existing such dataset—to facilitate finer-grain recognition with color information; and (b) a Vehicle Recognition in Video (VRiV) dataset, a first of its kind video testbench dataset for evaluating the performance of vehicle recognition models in live videos rather than still image data. The VRiV dataset will open new avenues for AI researchers to investigate innovative approaches that were previously intractable due to the lack of annotated traffic vehicle recognition video testbench dataset. Finally, to address the gap in the field, five novel metrics are introduced in this paper for adequately accessing the performance of vehicle recognition models in live videos. Ultimately, the proposed metrics could also prove intuitively effective at quantitative model evaluation in other video recognition applications. T One major advantage of the proposed vehicle search and continuous localization framework is that it could be integrated in ITS software solution to aid law enforcement, especially in critical cases such as of amber alerts or hit-and-run incidents.

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Section: Evaluation Metricsmentioning

confidence: 99%

“…These rules are, at least in part, established to help avoid responding to many data requests. To help assuage these issues, recently several assistive technologies have been proposed for applications such as incident detection [1,[3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%