Evaluating the Accuracy of 3D-AIM for Crowd Behavior Classification

Buckle up, folks! We’re diving headfirst into the world of artificial intelligence, specifically how well our very own 3D-AIM network stacks up against the big dogs in classifying crowd behavior. Think of it like this: we’re throwing a AI rager and inviting all the hottest models (the computer vision kind, get your head outta the gutter!) to see who can best predict what goes down in a crowd. Is it a mosh pit? A graceful waltz? Or maybe just your average, run-of-the-mill grocery store queue on a Saturday afternoon? Let’s find out!

Dataset and Experimental Setup: Setting the Stage for Our AI Smackdown

Before we unleash the algorithms, we gotta make sure the playing field is level, right? In the world of AI, that means carefully curating the data our models will be trained and tested on.

The Data: Crowd- Surfing Through the Crowd- Dataset

For this showdown, we tapped the Crowd- dataset, a treasure trove of video clips – almost six thousand! – depicting various crowd behaviors. We’re talking everything from the organized chaos of a marathon to the more, let’s say “animated,” gatherings you might find at a music festival. Think of it as the ultimate people-watching compilation, but instead of judging outfits, our AI is analyzing movement patterns and predicting the overall vibe.

Now, because we’re all about fairness, we randomly split the dataset into two groups: one for training our models (like teaching them the moves) and another for testing how well they’ve learned (think of it as the AI equivalent of a pop quiz).

Baseline Models: Calling in the Heavy Hitters

Okay, so we’ve got our data. Now, let’s introduce the contenders! In this corner, we have two established models known for their prowess in analyzing videos:

• I3D: This one’s a real veteran in action recognition research. Think of it as the seasoned pro, always down for a challenge.
• X3D-M: A leaner, meaner model known for its efficiency in recognizing human actions. This one’s all about speed and agility.

To keep things interesting (and because variety is the spice of life!), we’re testing both I3D and X3D using three different input types:

• RGB-only: Just like your eyes see the world – in glorious color!
• OF-only: This one focuses on Optical Flow, which basically tracks how pixels move between frames, giving us insights into motion patterns.
• Two-stream (RGB+OF): The best of both worlds! This approach combines color information with motion data for a more comprehensive analysis.

Model Modification: A Little Nip/Tuck for Our AI Contestants

Now, we wouldn’t be very good scientists if we didn’t ensure a fair fight, right? So, while we kept the core architecture of I3D and X3D intact (gotta respect the classics!), we did make a slight tweak. We adjusted the last classification layer to match the eleven classes in our Crowd- dataset. Think of it as tailoring their outfits for the big event!

Implementations: The Tech Behind the Curtain

Time for a little peek behind the scenes! We used a potent cocktail of PyTorch . and Python . for implementation, because let’s be real – what’s an AI showdown without some serious computing power? And of course, we implemented our very own 3D-AIM network, the star of the show, as depicted in the ever-so-stylish Fig. (You didn’t think we’d spoil the surprise and show it to you just yet, did you?)

Training Details: Whipping Our Models into Shape

Okay, imagine a montage here: AI models training hard, crunching numbers, sweating algorithms… you get the picture. We put these models through their paces for epochs, with a carefully chosen batch size, all powered by not one, but two Titan RTX GPUs. We’re talking top-of-the-line hardware here, folks! As for the training regime? We used a combination of random crops and flips (like a digital version of those fancy workout classes) to help our models generalize better. And to make sure they peaked at the right time, we used a cosine-annealing scheduler for our learning rate. Think of it as fine-tuning their training schedule for optimal performance.

Experimental Results: Let the Games Begin!

Alright, enough with the pre-show jitters! It’s time for the main event: seeing how our models perform under pressure.

Experiment : Classification Accuracy and Confusion Matrix Analysis

First up, we wanted to see how well each model could accurately classify different crowd behaviors. We’re talking about correctly labeling those video clips as “turbulent,” “crossing,” “merging,” you name it. And to make things even more interesting, we threw in a curveball: different loss functions.

Loss Functions: The AI Equivalent of a Fitness Regimen

In the world of AI, loss functions are like training regimes. They guide our models to learn from their mistakes and improve their performance. We tested three different loss functions:

• SoftMax: The OG of multiclass classification. It’s the classic training method that’s been around the block a few times.
• Binary Cross-Entropy (CE): This one’s a bit more specialized, designed for multilabel classification, where a single video clip can have multiple labels (because let’s be real, life is rarely black and white!).
• Our very own Separation Loss: This is our secret weapon, folks! It’s designed not only for multilabel classification but also to tackle the pesky problem of class imbalance. We’re talking about situations where some crowd behaviors are more common than others (like, you see a lot more casual strolling than, say, synchronized swimming in a crowd).

Evaluation Metric: Keeping Score in the AI Arena

Now, for the million-dollar question: how do we measure success? Simple: accuracy! We looked at which model correctly predicted the most crowd behaviors. Think of it as the ultimate AI popularity contest.

Findings: And the Winner Is…?

Drumroll, please! Our experiments revealed some fascinating insights. First off, it turns out that multilabel classification (using Binary CE and our Separation Loss) completely crushed it compared to good ol’ SoftMax. It seems that giving our models the flexibility to handle multiple labels is the way to go.

But the real MVP? Our very own Separation Loss! This bad boy achieved the highest accuracy, and we’re pretty sure it’s because of these two secret ingredients:

• Focal term: This clever addition helps our model focus on those tricky-to-classify examples, the ones that are rare but important (like spotting that one guy in a crowd doing the worm).
• Separation weight: This component is all about maximizing the distinction between different crowd behaviors. Think of it as drawing clear lines in the sand so our model doesn’t get its wires crossed.